Topics API Lab & Guides

Preserve Country and Political Context in Topic Data

Tue, 18 Aug 2026 12:00:00 +0000

Preserve Country and Political Context in Topic Data

A country name in an article can identify the location of an event, the jurisdiction of an institution, the origin of a quoted report, or a passing comparison. Treating every occurrence as the same kind of country topic makes search results confusing. Political coverage adds another layer: an institution, a public office, a policy subject, and a geographic area are related but distinct entities.

This guide proposes a hypothetical editorial data model for a multilingual publication. Its purpose is to organize documents and preserve their context. Start with the Country Topics API subject guide, then decide which geographic and institutional questions your archive should answer before choosing fields or labels.

Describe the role of each place mention

Give place relationships explicit roles. An event location answers where something occurred. An institution's jurisdiction describes the scope stated for that institution. A publication location identifies where a source was issued. A comparison reference identifies a place used to explain another subject. These roles should remain available even when the interface also offers a broad country filter.

Imagine a fictional municipal agency in Northhaven publishing a report about a bridge in Lakeford. An article discussing that report can have different values for issuing institution, publication location, and event location. A dateline should not overwrite the bridge's location. Nor should the bridge's location automatically define every institution mentioned in the article.

Record an evidence span for each relationship. If a place is implied but not named clearly, preserve the uncertainty and route it for review when the distinction matters. It is better for a record to contain an unresolved relationship than a precise geographic assignment supported only by an editor's assumption.

Keep geography and political entities distinct

Use separate record types for geographic areas, institutions, offices, organizations, and people. A government department can operate within a geographic jurisdiction while remaining a distinct institutional entity. A public official can hold an office during a particular period. Those relationships need their own evidence and timing, rather than being encoded as permanent properties of a country label.

The Political Topics API overview is a complementary subject area for organizing coverage of institutions, public policy, and political processes. A document might carry both a geographic topic and a political subject, but neither label should imply the publication's endorsement of the positions discussed.

For the hypothetical bridge article, the main subject may be public infrastructure administration. A reference to a legislative committee could be a substantial secondary topic or a brief mention. Decide from the article's actual treatment. The mere presence of an institution's name should not cause every document to be classified as a broad political analysis.

Record the source of geographic groupings

A geographic code becomes more useful when its namespace, source, and version are retained. Different reference systems may serve different purposes. Your internal identifier should remain stable while source codes and display labels can be updated through a documented process. Do not make a code string carry more meaning than its source defines.

The UN Statistics Division's M49 documentation describes country or area codes for statistical use. It explains that its groupings serve statistical convenience and do not imply political affiliation or opinions about territorial status. An editorial system using a reference vocabulary should preserve that stated scope.

When sources use different names or geographic frames, retain the source's terminology in the evidence record. A publication can choose a documented display policy while making the original wording inspectable. Avoid resolving a complex disagreement by silently replacing every source term with a single unexplained label.

Distinguish identity from presentation

A display name is a reader interface decision. An identifier connects records. A boundary definition describes a geographic concept under a specified reference. Keeping these functions separate allows a label correction without accidentally changing which documents belong to an entity.

Preserve multilingual evidence

Store the original passage and its language alongside any translation used during annotation. If a translation helps an editor identify a topic, record that transformation. Reviewers should be able to tell whether they are reading the source's words or an interpretation prepared for the workflow.

Maintain multilingual aliases carefully. A translated institution name, an abbreviation, and a transliterated name may refer to the same entity, but the connection needs evidence. Do not merge records merely because their translated names are similar. Generic names such as planning committee can describe many unrelated bodies.

In the fictional archive, two articles use different language versions of the Northhaven agency's name. An editor can link them after checking the issuing source and institutional context. If the abbreviated name remains ambiguous, retain both candidate interpretations for review instead of automatically choosing the entity with the most documents.

Test text normalization on representative examples. Search convenience should not destroy the original spelling or punctuation needed for review. Keep the original string even when a separate normalized form is used for matching.

For ambiguous names, return a short candidate list with the evidence that favors each match. Include the surrounding institution, document language, and geographic role where they are available. Popularity in the archive can help order a review queue, but it should not determine identity by itself. A small municipality deserves an accurate match even when a similarly named institution has many more records. Save the reviewer's decision as a reusable example, without treating that one decision as a universal rule.

Give relationships a time context

Institutional names, offices, and administrative relationships can change. A historical article should retain the context that its source describes. Store when a relationship is said to apply, when the source was published, and when the annotation was reviewed. These fields help explain why two apparently conflicting records may both be appropriate within different periods.

Suppose a fictional report describes a department before a later reorganization. A current display name may help search, but it should not erase the historical institution named in the report. Link the entities through a documented succession or renaming relationship only when the source supports it.

The topic schema guide offers a foundation for identifiers and versioned annotations. Apply the same discipline to geographic and institutional relationships so updates can be reviewed without rewriting the meaning of earlier coverage.

Keep political topic annotation at document level

Define subject labels around observable coverage: legislative procedure, public administration, diplomatic meetings, budget processes, or election administration. Provide clear inclusion and exclusion examples. The purpose is to describe what a document substantially discusses, with enough precision that readers can find related material.

Keep those classifications separate from assumptions about authors or readers. A document about a public policy does not establish a reader's political affiliation, and quoting a position does not establish an author's support for it. Attribute claims to the people or institutions identified in the source while preserving the article's own framing.

For contested or ambiguous passages, allow multiple sourced perspectives to remain visible within the document record. Editors can distinguish a direct quotation, an attributed summary, and the publication's analysis. This supports an archive that explains its evidence without flattening disagreement into a supposedly neutral sentiment score.

Evaluate context errors, not just names

A review set should contain shared place names, abbreviated institutions, translated titles, historical references, and articles comparing several jurisdictions. Ask reviewers to verify both the entity match and its role. A correct country name attached as the wrong event location still produces an incorrect record.

Use the news taxonomy guide to connect these checks with editorial labeling rules. Track the causes of disagreement: ambiguous source wording, missing context, translation uncertainty, or an overly broad taxonomy. The remedy depends on which problem occurred.

Review retrieval results across languages rather than relying only on overall label accuracy. If one language yields mostly institution matches while another yields mostly country matches, the issue may lie in annotation examples or matching rules. Inspect representative records before treating the difference as a meaningful pattern in coverage.

Conclusion: make geographic context inspectable

Country and political topic data becomes useful when roles, relationships, source terminology, language, and time remain visible. Start with distinct entity types and evidence backed links, then add the display conventions your publication needs. A well structured archive lets readers understand the context of a document while giving editors a clear path to review and correct its classifications.

Design Prediction Topic Contracts Without Inventing Forecasts

Tue, 09 Jun 2026 12:00:00 +0000

Design Prediction Topic Contracts Without Inventing Forecasts

A document about the future can contain a forecast, a target, a scenario, a conditional statement, and a question in the same paragraph. A topic classifier that labels all of them prediction loses the differences that matter most to a reader. The first design task is to describe what the document says without adding a prediction of your own.

This guide develops a hypothetical publishing contract for future oriented coverage. It describes document metadata and editorial review, not a forecasting service. The Prediction Topics API overview introduces the subject area; the contract below shows how a team could make its records precise enough for search, comparison, and later correction.

Start with the statement being represented

Separate the document topic from any statement extracted from it. A topic might be library digitization. A statement might describe a proposed completion date, a conditional scenario, or an author's expectation. The topic remains useful even when the document contains no quantified forecast at all. Keeping the two records distinct prevents an empty probability field from looking like a missing product feature.

Use statement types that editors can identify from the source. A target expresses an intended outcome. A scenario describes a set of assumptions and possible consequences. A forecast expresses an expectation attributed to someone. A question asks about an outcome without asserting it. A historical report describes what happened, even if it quotes an older forecast.

In a hypothetical article, a library says it aims to digitize an archive within two years, subject to funding. Represent that as a stated target with a funding condition. Do not convert the target into a probability of completion, and do not treat the classifier's confidence in the label as confidence in the outcome.

Give time several separate fields

At least three times may matter: when the source was published, when the statement was made, and the period the statement concerns. A fourth field can record when your system retrieved the source. These times can differ. A new article may quote an older presentation, and a retrieved page may have changed after its original publication.

The W3C Time Ontology in OWL describes instants, intervals, durations, and relationships among temporal entities. For an editorial contract, this is a helpful conceptual basis for distinguishing a statement date from an outcome interval. The example fields here are a proposed application design, not an implementation of the complete ontology.

Store the original time phrase alongside any normalized interpretation. If a speaker says next summer, keep that wording and identify the reference date used to interpret it. If the source does not provide enough context, preserve an unresolved horizon. A precise looking date created from an ambiguous phrase can be more misleading than a visibly incomplete record.

Preserve granularity

A year, a quarter, and a day carry different precision. Do not invent midnight timestamps for records that only identify a year. When intervals are useful, document whether their endpoints are inclusive and which calendar or timezone applies. Consumers should be able to distinguish a source's precision from convenience added by the storage system.

Keep uncertainty attached to its owner

There are several different uncertainties in this workflow. The source may be uncertain about an outcome. The extractor may be uncertain about the statement type. An editor may be uncertain about the identity of the speaker. Treat these as separate observations with separate owners. A single confidence number cannot explain them all.

If a source supplies a probability, preserve its wording, scope, and method when available. Record that the value is reported by the source. If no probability appears, use an explicit absent state rather than generating a number. Qualitative expressions such as possible or likely can remain text unless your publication has a documented, appropriate mapping supplied by that source.

For the hypothetical library article, the record might contain an unresolved horizon, a target statement type, and a funding condition. That is already useful metadata. Readers can find conditional plans without being shown an invented confidence score. The extractor can separately indicate that the target classification requires human review because the paragraph mixes intention and expectation.

Design the contract around inspectable evidence

A compact record can include a stable statement identifier, its parent document, subject identifiers, statement type, exact source span, attributed speaker, temporal fields, conditions, and review state. Add a schema version and extraction version so later changes can be traced. Store identifiers independently of display labels to support renaming without breaking links.

The topic schema guide explains the broader value of stable identifiers and explicit contract boundaries. Here, those boundaries matter because downstream applications may otherwise interpret a richly structured statement as an endorsed conclusion.

Subject: the thing being discussed, such as the hypothetical archive digitization project.
Claim context: who made the statement, where it appears, and the conditions included in the source.
Horizon: the original phrase, any supported normalization, and unresolved ambiguities.
Review: extraction status, editorial decisions, and the reason for a correction.

Keep explanatory notes readable. A reviewer should be able to see why a record is unresolved without reverse engineering an internal error code. Structured fields serve retrieval; concise notes preserve the reasoning behind difficult decisions.

Distinguish information that is not stated from information that is not applicable or unresolved. A target with no probability is different from a damaged source passage whose probability cannot be read. Give each missing field a reason when the distinction affects interpretation. Clients can then display a useful explanation, avoid unnecessary retries, and route only the records that need editorial attention. This also makes completeness metrics more honest: an intentionally absent value is not an extraction failure.

Handle revisions without erasing the past

A statement may be revised, withdrawn, clarified, or discussed after its horizon has passed. Preserve the original record and connect the newer evidence through an explicit relationship. An updated source date does not necessarily mean the statement changed. Compare the relevant passage before changing its meaning or lifecycle state.

Consider a fictional follow up article that narrows the library project to one collection. That could revise the scope of the earlier target. It could also describe a separate phase. An editor needs both source passages to decide. Automatically treating every later mention as a replacement would destroy useful context.

Use neutral lifecycle labels such as active coverage, revised statement, or awaiting outcome review. An elapsed horizon alone does not establish whether an outcome occurred. If a publication wants to evaluate outcomes, define a separate evidence process and keep its conclusions distinct from the original extraction.

Test ambiguity before scale

Create a review set with clear targets, explicit scenarios, quoted forecasts, rhetorical questions, and retrospective reporting. Include statements with several subjects or horizons. Ask reviewers to identify both the record type and the evidence span. Agreement on a broad topic can hide disagreement about the sentence that supposedly supports it.

Measure errors that affect interpretation: assigning a probability that is absent, dropping a condition, attaching a quote to the wrong speaker, or normalizing a horizon from the wrong reference date. These failures deserve more attention than minor differences in display wording. The structured extraction guide offers related ideas for validating records before an application uses them.

Keep an abstention path. If a paragraph combines several incompatible interpretations, a review queue is a valid result. A system that always emits a complete object may appear reliable while filling its most consequential fields with assumptions.

Make the reading interface match the contract

Display the statement type beside the source attribution. Put conditions close to the statement and show unresolved timing in ordinary language. Avoid a dashboard tile that collapses a target, a scenario, and a forecast into one apparent outcome metric. The visual hierarchy should help readers understand what was said and by whom.

Allow filtering by subject and horizon without implying that matching records are comparable predictions. Two documents can discuss the same project while referring to different phases, assumptions, or definitions of completion. The record view should make those differences discoverable before inviting comparison.

Conclusion: describe future oriented coverage faithfully

A reliable prediction topic contract preserves subjects, attribution, time, conditions, and uncertainty. It can make future oriented documents searchable without asserting that their outcomes will occur. Start with explicit statement types, retain the original evidence, and treat unresolved fields as meaningful information. That produces a useful editorial dataset whose limits remain visible to every consumer.

Prompt design for topic labels: definitions, evidence, and edge cases

Thu, 22 Jan 2026 12:00:00 +0000

Prompt design for topic labels: definitions, evidence, and edge cases

A topic-labeling prompt is an editorial decision rule expressed for a model. It should explain what to classify, which concepts are available, what evidence counts, and what to return when no assignment is justified. If those choices remain implicit, adding more forceful wording rarely resolves the underlying ambiguity.

The Prompts Topics API guide introduces the role of prompts in content classification. This article focuses on designing a reusable prompt and improving it through examples and review. The templates are hypothetical starting points. Their value depends on how well the vocabulary and evaluation match the documents your application actually processes.

Describe the decision in ordinary language

Begin with a task statement that a colleague could apply without additional explanation. “Assign subjects substantially discussed in this article using the supplied vocabulary” is more precise than “Find the best topics.” Add the intended use when it changes the decision: subject-page publication may require stronger evidence than a candidate list shown to an editor.

Define the input unit. If the model receives a title, summary, and body, state which fields support classification and how to handle disagreement between them. A dramatic headline may emphasize a passing detail. A summary may omit a secondary subject that the full text discusses at length. The prompt should reflect the same policy used by human reviewers.

Write an explicit rule for primary and secondary labels. For example, the primary topic can represent the article's central editorial purpose, while secondary topics require meaningful coverage in their own right. Avoid asking for an exact number of labels unless the application truly needs one. A quota can encourage weak additions to an otherwise reasonable result.

Supply definitions that resolve ambiguity

Give each allowed topic a stable identifier, a preferred label, and a concise definition. Add exclusions where neighboring concepts are easy to confuse. The word “bank” can refer to a financial institution or the edge of a river. An identifier helps software recognize the concept, while the definition helps establish which meaning belongs in the current vocabulary.

Keep definitions focused on subject matter. A banking topic might cover deposit-taking institutions and their services, while excluding incidental mentions of a bank as an event sponsor. A water-management topic might cover flood control, water supply, and management of waterways. These are proposed local definitions for an example taxonomy, not universal boundaries.

If two definitions overlap, decide whether both labels are appropriate or whether a hierarchy resolves the case. Prompt wording cannot settle an editorial policy that the team has not chosen. The news taxonomy guide explains how to document concepts and relationships before turning them into classification instructions.

Choose examples that reveal the boundary

Anthropic's prompting best practices recommends clear instructions and relevant, varied examples, with structure that separates examples from instructions. For topic labeling, use that guidance to demonstrate difficult boundaries. The examples should show the exact kind of evidence and response your application expects, while leaving room for evaluation on unfamiliar documents.

Start with a clear positive case: a detailed report on a bank's new savings account policy can fit the hypothetical banking definition. Pair it with a clear negative case: a river restoration story should not receive that label because the word “bank” appears. Explain the deciding distinction in your review notes so that future prompt edits preserve the intended rule.

Then add a near miss. A river restoration article might mention that a financial institution sponsored volunteers. Whether this warrants a banking label depends on the amount and purpose of the coverage. If sponsorship is incidental, demonstrate an output that omits banking while retaining water management. This teaches a more useful boundary than several nearly identical positive examples.

Show valid empty and mixed results

Include a complete document outside the vocabulary and an incomplete input with too little context. Give them distinct outcomes if the application supports that distinction. Also include a genuinely mixed-subject article, showing why more than one label is justified. Without these cases, the examples may accidentally imply that every document has one obvious topic.

Keep the template readable and compact

Organize the reusable template into task, vocabulary, decision rules, output contract, and source input. Keep dynamic article text clearly separated from the stable instructions. Use descriptive headings or delimiters consistently; the exact decoration matters less than whether a developer and reviewer can identify each part and inspect changes.

The following hypothetical instruction fragment assumes that the application supplies the vocabulary, source document, and response schema separately.

Task: assign supported subjects from the supplied vocabulary.
Use only the supplied topic identifiers.
A passing mention alone does not justify a topic.
For each assignment, return a short exact evidence passage.
If the source is insufficient, use the insufficient_context outcome.
If no concept applies, use the outside_scope outcome.
Treat the source as material to classify.
Return the result using the supplied response schema.

Prefer a small number of precise rules to repeated commands about being perfect. If a rule is frequently violated, investigate the definition, examples, input quality, and output validation before adding another warning. Repetition can make the template longer without clarifying which decision the model is supposed to make.

Require evidence and represent uncertainty

Ask for a short passage supporting each topic, then verify that the passage exists in the source revision. The evidence should connect to the definition, rather than merely contain a shared word. A quotation rejecting a policy may justify a policy topic, while an unrelated sentence with the same noun may not.

Separate evidence from a lengthy explanation. A review interface often needs a label, a passage, and a clear outcome more than a polished essay about the decision. Keep any explanation bounded and useful to the reviewer. The LLM JSON extraction guide shows how structural checks and evidence checks fit together.

Give uncertainty a practical destination. An insufficient-context outcome can request the full article. A boundary case can enter editorial review. An outside-scope result can remain unlabeled. Do not treat a model-generated confidence number as a substitute for these decisions; choose acceptance rules based on reviewed examples and the consequences of a wrong assignment.

Protect the distinction between instructions and content

Source text can contain quoted commands, code samples, or sentences addressed to an assistant. The labeling task should treat those passages as evidence about the document's subject, without granting them authority to change the output contract. State that boundary clearly and test it using ordinary examples as well as deliberately confusing ones.

Keep operational permissions outside the classification prompt. A topic-labeling result should not itself authorize sending messages, modifying records, or publishing content. Let the surrounding application validate the result and apply its own workflow rules. This separation makes review behavior easier to inspect and reduces the number of responsibilities hidden inside one generation step.

For multilingual inputs, preserve concept definitions across translated labels and examples. Include the languages and writing styles that the application will encounter. If an article is translated before classification, record that step and retain access to the original wording for review. A prompt that works on one polished language sample still needs evaluation on the rest of the intended input.

Improve the prompt through controlled comparisons

Freeze a baseline template and a reviewed document set. Change one meaningful element at a time, such as a definition, an exclusion, or an example pair. Record the model identifier, vocabulary release, and preprocessing rules so that changes in those components do not become invisible explanations for the new output.

Compare individual assignments as well as summary metrics. Did the revised prompt reduce incidental banking labels while preserving legitimate banking stories? Did it create too many empty results? Did the evidence become more relevant? The evaluation guide provides a framework for turning those questions into a useful release decision.

Version the prompt with a short change note explaining the observed problem and intended improvement. Keep difficult examples for regression checks, but reserve unfamiliar documents for evaluation. Revisit the prompt when the vocabulary or input source changes; an old rule may remain syntactically clear while no longer matching the editorial workflow.

Conclusion: make the policy easy to follow

A strong topic-labeling prompt expresses a clear policy through definitions, boundary examples, evidence requirements, and useful uncertain outcomes. Keep it readable enough for another editor to apply, and evaluate changes against real decisions. The prompt becomes easier to maintain when every instruction has an identifiable purpose and every revision responds to an observed problem.

Separate Social Topic Signals from Repeated Posts

Tue, 25 Nov 2025 12:00:00 +0000

Separate Social Topic Signals from Repeated Posts

A busy social feed can contain many appearances of the same underlying story. A post may be shared, quoted, copied, translated, or published again with a different introduction. Counting every appearance as independent evidence makes a topic look broader than the collected material can support. A useful workflow keeps repetition visible while separating it from distinct content.

This guide uses a hypothetical editorial dataset drawn from sources the publisher is authorized to process. It describes a possible analysis workflow, not a live social monitoring service. The Social Media Topics API guide introduces the subject area. Here, the focus is on defining what gets counted and preserving enough context to explain the result.

Choose the unit before counting

At least four units can matter: an activity, a content object, a distinct text, and an editorial story cluster. An activity records an action associated with content. An object identifies a particular post or item. A text fingerprint groups matching content under a declared normalization rule. A story cluster groups items that discuss the same specific story or event.

The W3C Activity Streams 2.0 specification provides a model for describing activities and associated objects. That distinction is useful conceptually even when a publisher's input format differs. Preserve the action and its object separately so repeated activity around one item is not mistaken for multiple independently created items.

In a toy dataset, 240 observed activities might refer to 80 content objects containing 36 distinct normalized texts across six editorial story clusters. These are invented numbers for illustration. Each count answers a different question. None establishes how many people agree with a story, how much independent reporting exists, or how representative the sample is.

Make exact matching deliberately conservative

Begin with source identifiers where they are available and reliable within their namespaces. Store the source namespace with the identifier so matching values from different systems are not merged accidentally. Preserve references to the original object when an activity points to it. Those links often provide stronger evidence than a text comparison alone.

For exact text matching, define normalization carefully. Removing repeated whitespace may be reasonable for one dataset. Removing punctuation, negation, or all links can change meaning. Keep the original text alongside the normalized representation and version the normalization function. A future reviewer should be able to reproduce why two items matched.

Apply the same care to URLs. Some parameters may identify tracking information; others may identify different content. Use a documented, limited normalization policy instead of deleting every query parameter. A canonical destination can help connect posts, but sharing a destination does not make their commentary identical.

Treat similarity as a proposal for review

Near duplicate detection can identify wording that differs slightly, but similarity is not identity. Two posts may use a common announcement template while describing different events. Another pair may express opposing interpretations of the same quotation. Keep candidate matches separate from accepted duplicate relationships until the chosen rule has been evaluated.

Imagine two hypothetical community library posts. One announces a temporary closure; the other corrects the reopening date. Their text may be highly similar. Merging them without retaining the correction would erase the most useful information. Store their relationship as an update when the evidence supports that interpretation.

Use a small review set to select thresholds and examine errors. Test short posts, long quotations, emoji heavy messages, translated material, and repeated templates separately. A threshold that works for one form of content may merge too aggressively in another. Preserve an uncertain state when the available evidence cannot settle the relationship.

Keep duplicates and story clusters separate

A duplicate group describes repeated or closely matching content. A story cluster describes shared story or event context. Several distinct reports can belong to the same story without being duplicates. Conversely, the same copied text can appear in contexts where the surrounding discussion differs.

The news topic taxonomy guide helps distinguish broad subjects from specific coverage. A library reopening can belong to public services as a topic and a particular reopening story as a cluster. The topic should remain stable even if the story cluster later splits into separate events.

Record the reason for a cluster relationship: a common event identifier, a shared source document, matching entities and timing, or editorial review. Avoid treating a similarity score as a complete explanation. Store competing interpretations when a collection contains both a general discussion and several related local events.

Preserve meaningful variation

Within a cluster, retain the differences readers care about: an update, a correction, a new source, or a distinct language edition. A compact display can show one representative item while allowing those variations to be inspected. Deduplication should reduce repetitive presentation without discarding the record of what actually appeared.

Keep a trace from every displayed cluster back to its member records. If a reviewer splits one cluster into two, update the cluster count while preserving the underlying activity count. A merge changes organization; it does not create or remove observed actions. Record the decision, the previous membership, and the clustering version so a later report can explain why its totals differ. The same trace helps an editor check whether a representative item still captures the cluster after an important correction or new document arrives. Choose that representative by a stated editorial rule, such as the clearest original report available in the collection.

Report observed patterns with their denominator

A topic count becomes interpretable when readers know the collection window, included sources, query rules, and unit being counted. State whether a chart describes activities, content objects, distinct texts, or story clusters. Avoid changing that unit between periods while presenting the result as one continuous measure.

Suppose the fictional dataset includes only a selected set of public service publishers. An increase in observed library coverage can describe that source set. It cannot establish an increase across all social media. A changed source list, collection interruption, or query adjustment may also change the count even when the underlying conversation is similar.

Keep coverage metadata beside the result. Record missing collection intervals and changes in source access. If comparison windows have materially different coverage, label the comparison as limited or withhold the calculated trend until it can be interpreted. A smaller, well explained table is more useful than a dramatic line whose denominator changed invisibly.

Use permission and provenance as input requirements

Define the approved source set and the permitted processing purpose before collection. Where relevant, retain a reference to the permission or agreement that governs the material. The analysis pipeline should know which records may be retained, transformed, or displayed under the publisher's established rules.

Keep source identifiers and retrieval context even when the interface displays only a short excerpt. If source content changes or becomes unavailable, the record needs a clear status rather than silently implying that the original remains accessible. Handle removals and retention through the publication's documented process.

The News Topics API subject guide provides adjacent editorial context. Social posts can point toward a story, but a topic match alone does not verify its claims. Preserve the distinction between finding relevant material and reviewing the evidence needed for publication.

Evaluate the workflow through representative failures

Build a labeled review set with exact copies, quoted comments, corrections, translations, independent coverage, and unrelated template matches. Evaluate duplicate decisions separately from story grouping. A system can perform well on exact copies while incorrectly grouping different events under one story.

The classification evaluation guide offers a useful structure for error review. Inspect false merges closely because they can hide new information. Inspect missed matches because they can inflate counts. Record which failure matters most for the specific interface or editorial task.

Review a sample of clusters after each meaningful rule change. Compare both aggregate counts and the items that moved. If one large cluster suddenly absorbs many unrelated posts, investigate before publishing a trend. Keep prior clustering versions available long enough to explain changes in a report.

Conclusion: count the thing you can explain

A careful social topic workflow separates activity, content, repetition, and shared story context. It uses explicit matching rules, preserves meaningful variation, and reports the limits of the observed sample. Start with a clearly defined unit and a modest review set. Expand only when the provenance and evaluation process can support the additional sources and complexity.

LLM topic extraction: a JSON contract you can actually validate

Thu, 04 Sep 2025 12:00:00 +0000

LLM topic extraction: a JSON contract you can actually validate

A language model can suggest useful topic labels, but an application needs a more precise agreement about what those suggestions mean. The response must identify the document, choose from the permitted vocabulary, and expose enough evidence for review. A neatly formatted object is useful only when the next component can decide whether to accept it.

The LLM Topics API guide introduces the broader extraction workflow. Here, the focus is the boundary between generated output and application data: a hypothetical JSON contract that can be parsed, checked, reviewed, and revised. The examples describe a design pattern for your own application, with no assumption that a particular model or hosted endpoint provides it automatically.

Choose the task and vocabulary first

Decide whether the model should discover candidate subjects or assign existing concepts. Discovery can suggest new wording for later editorial review. Classification should select from a known vocabulary. Mixing the two tasks in one unrestricted label array makes it difficult to distinguish a legitimate topic from a plausible phrase the model invented.

For classification, supply stable topic identifiers alongside concise definitions and exclusions. A display label by itself can be ambiguous. “Models” might refer to machine learning systems, statistical abstractions, or people in fashion photography. The definition should establish the intended meaning, while boundary examples clarify the cases most likely to cause confusion.

Also define how much evidence makes a topic relevant. Require substantive coverage if the result will populate subject pages. Permit weaker candidate matches only when a reviewer will inspect them before use. Make the primary-topic rule explicit, and set a reasonable maximum number of assignments. The Topics API schema guide explains these contract choices in more detail.

Describe the smallest useful result

Begin with document identity, a revision reference, an outcome, and an assignment list. Each assignment needs a permitted topic identifier and supporting evidence. Add fields only when a consumer can explain how it will use them. A long response packed with free-form analysis can be harder to validate than a small result with a few well-defined obligations.

The following invented schema illustrates a single assignment. It is intentionally limited to two sample topics so that the permitted values are easy to inspect.

{
  "type": "object",
  "properties": {
    "topic_id": {
      "type": "string",
      "enum": ["transport.public", "energy.solar"]
    },
    "evidence": {"type": "string"},
    "review_required": {"type": "boolean"}
  },
  "required": ["topic_id", "evidence", "review_required"],
  "additionalProperties": false
}

The official JSON Schema object reference explains that naming properties does not make them required. The required list specifies mandatory fields, while setting additional properties to false rejects undeclared fields in this simple object. It also distinguishes a missing property from a property whose value is null. Decide which representation each field accepts rather than treating them as interchangeable.

This schema checks shape and allowed identifiers. It does not establish that the evidence appears in the document or that the selected topic fits that evidence. Those are separate obligations for the surrounding application and review process.

Validate in several understandable stages

First, parse the output as JSON. If parsing fails, keep the failure visible instead of extracting whichever fragment happens to resemble an object. A permissive repair step can accidentally turn a partial or contradictory response into something that looks authoritative. If you choose to repair formatting, preserve the original output and label the repaired attempt.

Second, validate the parsed result against the chosen schema. Reject unknown identifiers, missing required fields, and values of the wrong type. Check nested objects as carefully as the outer envelope. For batches, attach every outcome to its input identifier so that a rejected result cannot shift the association of the remaining documents.

Third, apply application rules. Confirm that the taxonomy version exists, the document revision matches, the assignment limit is respected, and duplicate topic identifiers are handled consistently. A schema can express some of these constraints, but the application still needs access to the relevant vocabulary and document records.

Keep semantic review separate

Finally, inspect whether the selected topics are supported by the actual text. A structurally valid assignment of solar energy to an article about a company named Solar can still be wrong. Preserve this distinction in logs and evaluation reports: parsing failures, contract failures, and unsupported classifications call for different improvements.

Make the evidence independently checkable

Ask for a short source passage that supports each assignment. In the application, confirm that the passage occurs in the exact document revision used for classification. If your workflow permits normalized quotation matching, define the normalization rules explicitly. Otherwise, an apparently harmless punctuation change may hide a more substantial mismatch.

Offsets can help a review interface highlight the relevant passage, but define what an offset counts. Bytes, Unicode code points, and displayed characters are not interchangeable in every text. Choose a representation, test it with the languages you support, and keep the source text stable between extraction and rendering. An exact quoted span provides a useful additional check.

Require the evidence to support the chosen concept, not merely share a keyword. A sentence stating that a company does not operate solar installations should not justify a solar-energy assignment simply because the phrase appears. Include negation, quotations, historical background, and speculative statements in your review examples so that the evidence policy has clear boundaries.

Give uncertainty a valid output

A model needs an acceptable way to return no supported assignment. Distinguish a complete document outside the vocabulary from text that is too brief or incomplete to classify. If the application cannot represent these states, it implicitly pressures every input into a topic, even when the available evidence does not justify one.

Use explicit outcome names and document what follows from each. An insufficient-context result may trigger retrieval of the full article. An outside-scope result may be retained without an assignment. A review-required result may enter an editorial queue. A processing error should remain a processing error, with enough context to retry the correct input.

Keep source documents separate from application instructions. An article may contain quoted commands, code, or language that resembles a prompt. Treat that material as content to classify. Test whether your implementation preserves the boundary, and validate output independently; wording the boundary clearly is one part of the design, not proof that every result will obey it.

Handle long documents and repeated attempts deliberately

If you divide long documents into sections, decide how section-level assignments become a document-level result. Repetition in several chunks should not automatically make a subject primary. A long appendix may repeat a term more often than the main argument. Preserve section context and use a documented aggregation rule that reflects the article's purpose.

Record whether the input was shortened, cleaned, or translated before classification. A reviewer looking at the full article needs to know which text the model actually received. Retain a processing revision alongside the document revision when a change to extraction rules could affect the result.

Define a bounded retry policy. A formatting failure may justify another generation attempt, while missing source text requires a different remedy. Avoid repeatedly asking for a preferred label until it appears. Give each attempt an identifier and keep the selected result traceable to the input, prompt, vocabulary, and validation outcomes that produced it.

Evaluate the complete contract

Measure more than the share of responses that parse. Review unknown-label rejection, evidence matching, supported-topic accuracy, and the usefulness of uncertain outcomes. Test the complete path from document preparation to the final review screen. The classification evaluation guide shows how to connect these checks to representative documents and practical release decisions.

When changing the prompt or vocabulary, compare results on the same inputs. Inspect changed assignments and their evidence, especially where a candidate becomes more specific. Use the Prompts Topics API guide to organize instructions and examples around the exact boundaries your evaluation identifies.

Conclusion: connect every label to a check

A useful extraction contract makes each obligation visible: valid structure, permitted concepts, current document identity, and supporting evidence. Let uncertainty remain explicit, and keep structural checks distinct from semantic review. That gives developers a dependable integration boundary and gives editors a clear reason to accept, revise, or reject each proposed topic.

Build an AI Model Taxonomy That Keeps Claims in Context

Fri, 16 May 2025 12:00:00 +0000

Build an AI Model Taxonomy That Keeps Claims in Context

A model catalog becomes difficult to search when every announcement receives the same broad AI label. A text classifier, an image generator, and a retrieval system may appear in the same article, yet they play different roles. Developers need those distinctions to build useful filters. Editors need them to describe the subject accurately. Readers need them to understand what a reported result actually establishes.

The following design uses a hypothetical technical publication with a small model directory. It is an editorial data model, not a claim about a live TopicsAPI service. Start with the concepts on the AI Model Topics API page, then keep every category tied to a question that your catalog should answer.

Separate models, systems, and articles

Define three record types before assigning labels. A model record describes a particular model or version. A system record describes an arrangement of components, such as a model combined with retrieval, a database, and a user interface. An article record describes a piece of coverage that may discuss either or both. Giving these records separate identifiers prevents an integration feature from becoming an unsupported property of the underlying model.

Suppose a fictional document assistant searches an archive, extracts passages, and writes summaries. Its search coverage belongs to the archive and retrieval configuration. Its response formatting belongs partly to the application contract. Its summary behavior should be evaluated in that complete setting. If an article calls the assistant multilingual, preserve that statement as a sourced claim until the relevant languages and test conditions are documented.

This separation also improves navigation. A reader interested in summarization can discover the article without being told that every mentioned component performs summarization independently. Relationships carry the explanation that a single category cannot.

Use independent classification axes

A practical model taxonomy can use several small dimensions instead of a single enormous tree. Give each dimension a clear purpose and allow multiple values where appropriate. Do not force unrelated distinctions into a shared parent and child relationship simply because the interface needs another filter.

Task: the activity under discussion, such as classification, extraction, translation, generation, or ranking.
Modality: the form of input or output discussed in the source, such as text, image, audio, or a documented combination.
Context: the application setting, such as editorial review, document search, or educational experimentation.
Evidence: whether a capability is described, demonstrated in an example, evaluated on a stated dataset, or left unspecified.

These dimensions support precise combinations without implying an ordering of overall intelligence. A text input label says nothing about accuracy. A demonstration label says nothing about deployment readiness. Treat missing information as unknown instead of automatically filling every available field.

The LLM Topics API overview gives a useful adjacent subject area. A language model label can identify the main subject while extraction or summarization labels describe the particular task discussed.

Attach capabilities to evidence

For each capability assertion, store the source passage, the subject version, the reported task, and the conditions that limit interpretation. Use a separate evidence record so one claim can have several supporting or conflicting documents. Keep the source author's characterization visible instead of turning promotional wording into the publication's own conclusion.

The research paper Model Cards for Model Reporting proposes documentation that accompanies models and describes intended uses and performance under relevant evaluation conditions. That supports a useful editorial principle: a capability label should lead readers toward its context, rather than appearing as an unexplained badge.

In the hypothetical directory, a source may report extraction performance on short English product descriptions. The catalog should retain that scope. It should not silently extend the label to scanned contracts, other languages, or long documents. If an article discusses a limitation, that passage can be valuable evidence too. Recording limitations makes the directory more informative and reduces repeated editorial investigation.

Make the evidence status understandable

A status such as reported is more helpful when the interface explains who reported it. Separate source attribution from your own review state. Editors might mark a claim as awaiting review while still accurately displaying that a named document contains it. Those two states answer different questions and should never overwrite each other.

Keep AGI discussion separate from measurable scope

An article can discuss artificial general intelligence without demonstrating that a particular system satisfies any definition of it. Use an AGI discussion topic to describe that subject matter. Store the definition or criterion used by the source when one is provided. If the source gives no testable definition, retain that absence instead of inventing a threshold for the catalog.

For example, imagine a panel transcript that debates whether flexible task performance should count as general intelligence. The article may belong in the AGI Topics API subject area. The associated model record should still list only its documented task evaluations. A topic label is a retrieval aid, while a capability assertion requires its own evidence and scope.

Avoid an ordinal field that places all models on a universal path toward AGI. Such a field embeds assumptions that a reader cannot inspect. More useful questions are concrete: Which task was measured? What inputs were allowed? What assistance was provided? Where did the evaluation fail? Which claims remain untested?

Represent versions and lineage explicitly

Names alone are unreliable identifiers for an evolving catalog. Assign an internal identifier to each subject and maintain display names as editable metadata. A family record can group related versions, but evidence attached to one version should not automatically apply to its siblings. Likewise, a changed application configuration deserves a distinct evaluation context even when the underlying model stays the same.

In the fictional directory, Atlas Text is a family name and Atlas Text revision B is a particular subject. An article may mention only the family. Preserve that granularity instead of choosing the newest revision by default. If an editor later resolves the reference, record the correction and the evidence that justified it.

Keep aliases, release labels, and relationships in separate fields. An alias helps search find a record. A relationship explains derivation or grouping. Neither proves that two versions behave identically. This distinction prevents a simple name cleanup from changing the meaning of earlier coverage.

Evaluate the taxonomy with realistic questions

Review taxonomy quality through retrieval tasks, not just the neatness of its diagram. Ask an editor to find articles about image inputs used for document extraction. Ask another to find model claims with unspecified evaluation languages. If the required records are difficult to identify, inspect the missing dimensions or inconsistent annotation rules before adding more categories.

Build a small review set containing comparison articles, announcements, critical analyses, and documents that mention models only in passing. Record disagreements about the main subject separately from disagreements about capabilities. The topic classification evaluation guide provides a broader framework for checking label decisions and review thresholds.

Pay attention to negative examples. A headline about an AI company might concern its office move rather than any model. A tutorial might mention a generator while mainly explaining image licensing workflows. Clear exclusion examples help annotators apply the taxonomy consistently without labeling every nearby word as a major subject.

Publish definitions and preserve changes

Each label needs a short definition, an inclusion example, an exclusion example, and an owner who can resolve ambiguity. Record the taxonomy version with each annotation. When a definition changes materially, evaluate whether older records need review. Do not rewrite historical classifications silently, especially when readers depend on stable filters or export files.

A small change log can explain that a task was split into two narrower labels or that a display name was clarified. Keep retired identifiers resolvable so old links remain useful. The goal is a catalog whose meaning can be reconstructed, including the decisions that were reasonable before a later revision.

Conclusion: make every label answerable

A useful AI model taxonomy helps readers ask specific questions about subjects, tasks, versions, and evidence. Its strength comes from explicit boundaries and inspectable claims. Start with a compact set of independent dimensions, preserve uncertainty, and expand only when real retrieval needs justify a new distinction. That approach gives an editorial team a catalog it can explain as well as maintain.

Evaluate AI topic classification before you trust the labels

Wed, 12 Feb 2025 12:00:00 +0000

Evaluate AI topic classification before you trust the labels

An AI topic classifier becomes useful when its labels support a specific decision. That decision might place an article in a reading list, send a document to an editor, or help a researcher find coverage of an emerging subject. Evaluation should ask whether the classifier supports that decision reliably enough, with errors that the team understands and can manage.

A single score cannot tell you whether a system confuses adjacent subjects, misses short documents, or confidently labels articles that fall outside its vocabulary. Begin with the AI Topics API overview, then use this guide to design a practical evaluation around your own content. Every numerical example below is hypothetical, and no result represents a TopicsAPI service benchmark.

Write the acceptance rule first

Before collecting examples, describe what counts as a correct assignment. A policy article mentioning a university is not necessarily about education. A story discussing an electric bus fleet might legitimately need both transport and energy labels. Reviewers need a shared rule for substantive coverage, passing mentions, and documents with several important subjects.

Separate the primary-topic decision from the presence of secondary topics. A classifier might identify all relevant subjects while choosing an unhelpful primary label for navigation. Score those behaviors separately if the interface uses them differently. Otherwise, improvements to secondary coverage could hide a decline in the main category that readers actually see.

Document acceptable uncertainty too. A short, incomplete brief may deserve a request for more context. An article outside the vocabulary may deserve no label. Treat those as intentional outcomes when your workflow allows them, while keeping input failures separate. The Topics API contract guide explains how to represent these distinctions without overloading an empty array.

Build a review set that resembles the work

Collect examples from the inputs the application will really receive. Include long features, short updates, mixed-subject articles, and text with normal formatting noise. If the production input contains a title and body, preserve both in the evaluation set. Testing clean summaries while processing raw article text answers a different question from the one your users face.

Record useful attributes alongside each document: language, publication format, length range, broad subject, and whether it has been revised. These fields allow later inspection without changing the reference labels. Keep the original text or a stable revision reference so that a future reviewer can reconstruct the actual input.

Choose samples from separate stories, rather than allowing many near-identical updates to dominate the set. Ten versions of one announcement should not count as ten independent demonstrations of broad coverage. Place closely related versions together when dividing development and evaluation material, and explain how you handled repeated or syndicated text.

Include deliberate boundary cases

Add documents that distinguish neighboring labels. For an energy taxonomy, that might include a solar installation report, a utility earnings story, and a profile of a company whose name contains “Solar.” These examples help expose the reasoning implied by the vocabulary. Keep their role visible: a challenge set tests known boundaries, while a representative sample estimates ordinary workflow behavior.

Create reference labels through review

Have reviewers apply the written definitions before looking at model output. Otherwise, a plausible machine suggestion can become an anchor for the reference answer. For important boundary cases, ask two people to label independently, then resolve disagreements by recording the evidence and the rule that decided the case.

A disagreement does not automatically mean one reviewer was careless. It may reveal an overlapping definition, an incomplete article, or an unresolved editorial policy. Preserve such cases in a disagreement log. If a definition changes, review the affected examples and create a new version of the reference set rather than silently editing yesterday's answer.

Record unsupported assignments as well as missing ones. A reviewer should be able to say that a document supports energy but does not support finance, even though a company name appears. This negative evidence makes recurring confusion easier to diagnose and provides useful material for improving topic-labeling prompts.

Measure precision and recall for each topic

Google's classification metrics guide defines precision as correct positive predictions divided by all positive predictions, and recall as correct positive predictions divided by all actual positives. Both describe performance at a chosen decision threshold. Overall accuracy can conceal poor behavior when a topic is rare, because correctly rejecting many irrelevant documents can dominate the total.

Suppose a hypothetical evaluation contains twenty articles that truly concern solar energy. A classifier assigns the solar label to eighteen articles: fifteen correct matches and three unrelated articles. It misses five relevant articles. Precision is fifteen divided by eighteen, approximately eighty-three percent. Recall is fifteen divided by twenty, seventy-five percent. These figures describe different consequences: unnecessary inclusions and missed coverage.

Show the counts beside the percentages. A result based on a handful of examples deserves different confidence from one supported by a large and varied sample. If a topic has no positive predictions or no reference positives, report the missing denominator explicitly and explain your reporting convention. Do not let a software default create an impressive but meaningless number.

Inspect the errors behind the metrics

Group mistakes by cause. Useful categories include passing-mention confusion, neighboring-topic confusion, unsupported specificity, missed secondary topics, and insufficient context. These categories connect a score to an action. A broad labeling definition needs different work from a text-extraction problem that removed the paragraph containing the main subject.

Look at the same categories across document slices. A classifier might work well on long English articles yet struggle with brief bilingual updates. You do not need a separate dashboard for every attribute, but you should examine the dimensions that matter to your users. Combine very small slices carefully and retain the underlying counts.

Review successful cases too. A correct label attached to irrelevant evidence may be a lucky result that will fail on the next document. Ask whether the cited passage actually supports the topic definition. Evidence review is particularly helpful in LLM topic extraction, where fluent explanations can make weak decisions look persuasive.

Tune decisions without moving the test

If your classifier exposes a useful score, experiment with decision thresholds on a development set. Decide how you will trade additional review work against missed documents. A public subject page might demand stronger evidence than an internal discovery queue, so the two destinations may need different acceptance rules even when they use the same underlying classifier.

Keep a separate evaluation set for the final comparison. Repeatedly inspecting its failures and rewriting prompts around them turns it into development material. When that happens, label it honestly and reserve fresh examples for the next release decision. Preserve the old set for regression checks without pretending it remains untouched.

Do not interpret a generated confidence number as a measured probability by default. Compare score ranges against observed correctness on reviewed examples, and document what the score actually represents. When evidence is thin, a clear review state may be more useful than a precise-looking decimal that nobody can defend.

Make release comparisons actionable

Compare candidate versions on identical document revisions and reference definitions. Record the model identifier, prompt revision, vocabulary release, preprocessing rules, and decision thresholds. Then review which individual assignments changed. An improved aggregate score can still introduce a serious new confusion in the subject that matters most to one editorial team.

Set acceptance criteria before choosing a winner. A hypothetical team might require fewer unsupported primary labels, no regression on its essential subject categories, and a review queue that fits the editors' capacity. Those are local product decisions, not universal thresholds. State their rationale so that the next team member can judge whether they still fit the workflow.

Conclusion: make evaluation a decision record

A useful evaluation connects definitions, examples, errors, and consequences. Keep the reference set versioned, show counts with metrics, and inspect the evidence behind both successes and failures. The goal is a clear account of where topic classification helps, where people still need to review it, and what the next change should improve.

Classify Financial Documents Without Losing Entity and Time Context

Thu, 07 Nov 2024 12:00:00 +0000

Classify Financial Documents Without Losing Entity and Time Context

A financial document can mention revenue, a subsidiary, an accounting period, and a proposed project without making any recommendation about an investment. A good classifier preserves those subjects and their context. It should help a reader locate the right document, understand what kind of document it is, and inspect the passage behind each label.

Consider a hypothetical publisher building a searchable archive of business reports and educational explainers. The workflow here concerns document organization, not valuation or investment advice. The Finance Topics API overview provides the subject context. The design below separates the dimensions that a reliable archive needs to keep distinct.

Classify the document's purpose first

Begin with document type. An annual report, a press announcement, a transcript, an opinion article, and a tutorial serve different purposes. Their vocabulary can overlap extensively. A tutorial about reading cash flow statements should not become a company results record simply because it contains financial terminology and a sample table.

Next identify the document's main subject and any substantial secondary subjects. A company report might discuss operations, financing, governance, and risks. A short article may focus only on a reporting policy change. Keep a threshold for secondary topics so passing mentions do not overwhelm search results with weak matches.

In the hypothetical archive, Cedar Quay Manufacturing publishes a report and a separate announcement about an office relocation. Both documents concern the same fictional company, but their subjects differ. The relocation announcement belongs in organizational coverage. It should not inherit financial performance labels simply because another document about the company carries them.

Write annotation guidance around reader intent. Ask what a user would reasonably expect to learn after opening a result with this label. That question is more useful than counting how often a financial word appears.

Separate topics, entities, and observations

A topic describes a subject such as accounting methods or operating expenses. An entity identifies a company, organization, instrument, or other named subject. An observation records something stated in a document, such as a quantity and its context. These records can be connected without being collapsed into one object.

The SEC's EDGAR API documentation describes submissions history by filer and extracted XBRL data from financial statements. These are distinct source structures. For a publishing archive, that distinction is a useful reminder to preserve document context when working with more granular reported data.

Suppose a fictional report contains a table labeled operating expenses. Assigning an operating expenses topic does not validate the table, compare it with another period, or establish its significance. If a separate extraction workflow records numbers, it needs additional fields and checks. Keep its output separate from the topic annotation so consumers understand which operation actually occurred.

Make the record type visible

A user interface can call one item a topic match and another a reported observation. The difference should remain visible in exports too. Reusing a generic value field for both categories creates ambiguity that downstream systems may resolve incorrectly.

Retain every relevant time

Financial coverage often combines several temporal references. A document has a publication date. It may discuss a reporting period, mention an event date, and compare earlier periods. Your system also has a retrieval time. Store these separately and explain which field drives a given filter or sort order.

In the hypothetical archive, an article published in November reviews a report covering the preceding quarter. A search for documents published in November should include the article. A search for reporting periods ending in November should not include it solely because of its publication date. This distinction is simple to describe and easy to lose in an implementation.

Preserve the original period wording and its source location. If a document gives only a fiscal year label, avoid assuming a calendar year boundary. If an amendment or correction changes the source, record a new version and connect it to the earlier one. A later retrieval date alone should not make the underlying reporting period appear more recent.

When a companion extraction workflow records an amount, preserve the unit, currency, period, and source table context together. A bare number loses the information needed to interpret it. In the fictional report, a table caption might say that all values are expressed in thousands. Keep that caption linked to the observation instead of silently rewriting its magnitude. The topic classifier can identify the table as relevant coverage while leaving numerical reconciliation to the separately defined extraction and review process.

Resolve entity identity carefully

Give each entity a stable internal identifier and keep names as display metadata. A company can appear under a shortened name, a former name, or the name of a subsidiary. Those strings are clues, not proof that every mention refers to the same organization. Store aliases with the evidence and scope that support the relationship.

For Cedar Quay Manufacturing, imagine a fictional subsidiary named Cedar Quay Logistics. A parent company report may discuss both. Preserve their distinct identifiers and the stated relationship. An article about the subsidiary's warehouse should not automatically describe every operation of the parent organization.

When a recognized source supplies an identifier, retain its namespace as well as its value. Identifiers from different systems can look similar while identifying different things. If the source is ambiguous, return an unresolved entity mention for review. A search interface can still display the source wording while preventing an uncertain match from contaminating a company archive.

Build a taxonomy around retrieval needs

Choose categories that answer useful editorial questions: Which documents discuss reporting methods? Which explain financing concepts? Which cover corporate governance? Keep document types in their own dimension so a reader can combine a subject with an explainer, report, or announcement filter.

Use the topic schema guide to keep identifiers and versions consistent across those dimensions. A label definition should specify its scope, nearby concepts that can be confused with it, and examples of passages that are insufficient for assignment.

For example, a broad finance label might be appropriate for an introductory tutorial. A narrower accounting policy label needs substantial discussion of that subject. Avoid adding directional labels such as promising or concerning as though they were neutral topics. Such language introduces an interpretation that belongs, if used at all, in clearly attributed commentary rather than the taxonomy.

Preserve the evidence behind a label

Attach each important annotation to a source passage or section reference. Store document identity, version, language, and extraction method alongside it. Editors should be able to inspect the relevant material without searching a long report from the beginning every time a classification is questioned.

Evidence should support the assigned topic, not merely contain its words. A paragraph that lists topics excluded from a report does not establish that the report substantially covers them. Similarly, an example of an incorrect accounting interpretation in an educational article should not be stored as the publisher's factual conclusion.

A review note can explain the boundary: the article discusses disclosure structure, while the company appears only as a hypothetical example. This makes future corrections easier and gives new annotators an example of the intended standard.

Test for errors that change meaning

Build evaluation cases around common confusions. Include parent and subsidiary names, multiple reporting periods, corrected documents, educational examples, and articles that quote another source. Review both the main topic and the entity links. Good topic accuracy cannot compensate for attaching the document to the wrong organization.

Use the classification evaluation guide to structure a repeatable review process. Track disagreements by cause, such as weak subject evidence, entity ambiguity, or time normalization. A single aggregate score can conceal the particular mistake that makes an archive hard to trust.

Test the resulting interface with real retrieval questions from your editorial team. Can someone find tutorials about reporting periods without receiving only company announcements? Can they distinguish a corrected document from its predecessor? These tasks reveal whether the data model serves readers as well as passing field validation.

Conclusion: organize documents with their limits intact

Financial topic classification works best when it keeps document purpose, subjects, entities, and time in separate, connected fields. It should preserve evidence and make ambiguity visible. Begin with a compact taxonomy, test meaningful retrieval tasks, and keep any numerical analysis in its own clearly defined workflow. Readers then receive a useful archive of financial coverage without unsupported conclusions being smuggled into its labels.

Build a news topic taxonomy that survives the next news cycle

Tue, 27 Aug 2024 12:00:00 +0000

Build a news topic taxonomy that survives the next news cycle

A newsroom taxonomy should help people find related reporting even when the latest headline has disappeared from the homepage. That requires concepts with enough continuity to connect stories over time, plus enough flexibility to describe subjects that editors are only beginning to cover. A growing list of tags can satisfy neither goal if nobody agrees on what each tag means.

Start with the editorial jobs the vocabulary will support: routing copy, building subject pages, suggesting related reading, or comparing coverage. The News Topics API guide introduces those workflows. This article develops a practical taxonomy for them, using a hypothetical local newsroom that covers transport, education, housing, and public policy.

Separate subjects from the other ways news is organized

A section answers where a newsroom publishes a story. A subject describes what the story substantially concerns. A location identifies a place with a defined relationship to the reporting. A genre describes the editorial form, such as analysis or an interview. A named entity identifies a person or organization. Keep these dimensions distinct even when the same words appear in several of them.

Imagine an analysis of a city's decision to buy electric buses. It might appear in a local section, concern public transport and procurement, mention the city government and a vehicle manufacturer, and focus on a particular municipality. Combining all those values into an undifferentiated tag list makes it harder to build predictable filters or explain related-story recommendations.

The distinction also prevents misleading assumptions. The location of a publisher does not establish where an event occurred. A politician's name does not prove that electoral politics is the main subject. A business desk may publish a story primarily about workplace safety. Define the relationship behind each metadata field, and let editors inspect it.

Begin with a compact set of useful concepts

Inventory a representative sample of articles and ask editors which recurring subjects readers would reasonably want to follow. Begin with the concepts that serve those reading needs. A small vocabulary with clear boundaries is easier to apply consistently than a large collection of impressive labels that overlap or rarely receive a meaningful assignment.

Give each concept a preferred label, a stable identifier, a definition, and a short note on exclusions. For public transport, the definition could include planning, provision, and operation of shared passenger services. An exclusion might distinguish private vehicle sales. These are proposed editorial choices for the hypothetical newsroom; another publisher may draw the boundary differently.

Add examples that test the boundary. A bus timetable change clearly fits public transport. A manufacturer's quarterly results may fit business finance, even if buses are its main product. An article about a government bus purchase may support several topics. The purpose of examples is to make the rule usable, not to exhaust every story an editor will encounter.

A hierarchy can connect broad subjects to narrower concepts. A reader following transport may want to discover public transport, cycling infrastructure, and road planning. Decide whether assigning a child topic automatically makes a story eligible for its parent page. Then document the rule so that archive behavior does not depend on the particular editor who handled the article.

Allow depth where it serves coverage. A specialist climate publication may need several distinct energy-policy concepts, while a small general newsroom may prefer a broader energy category. Avoid forcing every branch to the same number of levels. A tidy diagram is less important than clear distinctions and enough relevant reporting to support each destination.

Keep temporary story collections separate

A named summit, court proceeding, or municipal election can deserve a dedicated collection without becoming a permanent subject category. Give event collections their own identifiers and relationships to enduring topics. A transport funding vote can belong to both a time-bound election collection and a lasting public transport subject page without making either structure carry the other's job.

Consider an external vocabulary carefully

IPTC Media Topics provides a subject taxonomy focused on categorizing text, with a hierarchy and machine-readable distribution formats. It offers a useful reference when a newsroom wants shared concepts or a vocabulary to compare with its own. The existence of a standard does not decide which concepts your readers need or how your editors should apply them.

Build a mapping table between local concepts and the external vocabulary. Mark whether each relationship is equivalent, broader, narrower, or only related. Do not label a mapping exact merely because two names look similar. Read the definitions, compare example articles, and preserve notes explaining where the boundaries differ.

Record the vocabulary release used for the mapping. When either side changes, review affected relationships instead of assuming the old mapping remains valid. The same principle applies to an internal reorganization: a stable concept identifier and a recorded version make archive interpretation much clearer than a display label alone. See the JSON contract guide for the underlying data model.

Handle evolving stories without losing history

News articles change as reporting develops. An early brief may focus on a transport delay; a later revision may establish that a procurement dispute caused it. Associate topic assignments with a document revision and record whether an editor accepted, removed, or replaced them. That history explains why a story appeared in one collection yesterday and another today.

Keep a distinction between adding a supported secondary topic and changing the primary subject. An update with a paragraph of background does not necessarily change the article's main purpose. Ask reviewers to consider the headline, opening, substantive evidence, and editorial intent together. Where they disagree, preserve the reason so that the taxonomy rules can improve.

Repeated updates also need a collection policy. A topic page flooded by minor versions of the same story may be technically complete but unhelpful to readers. Decide when to show the latest version, link a developing story thread, or display separate articles. The deduplication guide explores related decisions about repeated and overlapping content.

Make language and geography explicit

Use a shared concept identifier with language-specific display labels when the definition is intended to remain equivalent. Ask someone with relevant language and subject knowledge to review the translation. A literal translation can be grammatical while carrying a different institutional meaning, especially for public bodies, education systems, or government offices.

Store the original article language separately from the label language shown to a reader. A Spanish-language interface can display a translated topic label while the article remains in English. If a classifier works from a translated article, keep that processing choice visible because reviewers may need to inspect the source wording.

For location, define roles such as event location, subject jurisdiction, and places mentioned. A story about a national regulation discussed at a local meeting can legitimately contain all three. Avoid deriving political affiliation or a person's identity from a place label. The country topic guide develops ways to preserve useful geographic context.

Give changes an editorial owner

Choose who can propose a topic, who approves its definition, and who reviews effects on existing content. A lightweight change record can include the problem, proposed concept, example stories, overlapping concepts, and expected use. This keeps a one-off naming request from quietly becoming a permanent archive commitment.

Use actual confusion as a signal for improvement. If editors repeatedly disagree between two topics, review the definitions and the examples before adding another category. If a label stays unused, ask whether the newsroom lacks coverage, the wording is unclear, or the concept is too narrow. Each explanation suggests a different response.

Try a revision on a small archive sample before applying it broadly. Inspect the subject pages it would produce, including empty or very sparse destinations. Preserve old assignments when they remain meaningful, and mark cases requiring fresh review. A migration should make editorial behavior clearer, with enough context to reverse a mistaken change.

Conclusion: treat the taxonomy as an editorial product

A useful news taxonomy joins stable concepts to the newsroom's everyday decisions. Keep subjects distinct from people, places, sections, and temporary collections. Write definitions that editors can apply, version the important changes, and review the resulting reading experience. The vocabulary earns its place when it helps a reader follow a subject and helps an editor explain why a story belongs.

Topics API design: IDs, labels, and useful JSON contracts

Tue, 19 Mar 2024 12:00:00 +0000

Topics API design: IDs, labels, and useful JSON contracts

A useful topics API gives a document a stable place in a larger collection. It lets a search interface filter by subject, a publisher build a focused reading list, and an analyst compare coverage across time. The difficult work begins before the first endpoint: deciding what a topic means, how an assignment is justified, and which parts of the contract can change.

This guide uses “topics API” to mean an interface for classifying content against a defined vocabulary. Google's browser Topics API documentation describes a separate interest-based advertising mechanism. The examples here concern document subjects, rather than browser interest signals. Start with the broader Topics API foundations if you are choosing the scope of your own system.

Define the decision before designing the payload

Write one sentence describing what the classification will enable. “Route incoming articles to editorial queues” leads to different choices from “support a public archive of long-term subjects.” The queue may accept broad, provisional labels because an editor reviews every result. The archive needs consistent concepts, clear revision history, and rules for replacing outdated assignments.

Next, identify the unit being classified. A headline, full article, paragraph, transcript, and collection are different inputs. A headline about a council vote may omit the transport policy discussed in the body. If your contract accepts either headline-only or full-text inputs, record that distinction explicitly. A consumer should not have to infer evidence coverage from the number of returned topics.

Finally, decide whether you need one label or several. A story about a university's solar installation could reasonably concern education, energy, and construction. A single primary topic can support navigation, while secondary topics support discovery. Define how the primary choice is made so that “primary” does not become a convenient but unexplained ranking.

Separate identifiers, labels, and definitions

Give every concept an identifier whose meaning survives routine wording changes. A label is the wording a reader sees; an identifier is the value another system stores. If an editor changes “Solar power” to “Solar energy,” a stable identifier lets existing assignments remain connected without rewriting every document. Avoid embedding the entire category path inside the identifier if reorganization is likely.

A definition should describe the inclusion boundary. “Reporting substantially about electricity or heat derived from sunlight” is more actionable than “Everything about solar.” Add an exclusion note for a common confusion, such as a fictional company named Solar. Include a few representative examples, but do not let examples replace the definition: future documents will always find a new edge case.

Keep aliases separately. “Photovoltaics” might help searchers discover the concept, while the preferred label stays short. An alias can also be narrower than a topic, so document whether it is an exact synonym or simply a retrieval aid. Treating every helpful search term as a new topic creates duplicates that later complicate reporting.

Keep concepts apart from assignments

A topic record describes a concept. An assignment describes the claim that a particular document belongs to that concept. Combining them into one mutable object makes revision difficult: changing a topic label should not silently change the evidence or review status attached to a document. Keep the vocabulary and the classification results connected by identifiers.

The following hypothetical response illustrates that separation. The identifiers, release names, and values are invented for this guide; they describe a possible application contract.

{
  "document_id": "article-solar-campus",
  "document_revision": "r3",
  "taxonomy_version": "2024-03",
  "status": "review_required",
  "assignments": [
    {
      "topic_id": "energy.solar",
      "role": "primary",
      "evidence": "The campus will install rooftop solar panels."
    }
  ]
}

The document revision matters because an edited article may no longer support the same assignment. The taxonomy version identifies the definitions used at decision time. A review status tells consumers what they may do next. The evidence gives a reviewer something concrete to inspect. None of these fields alone establishes correctness; together, they make the decision easier to examine.

If you include scores, explain their origin and interpretation. A ranking value, a calibrated probability, and a model's self-reported certainty are different things. Name the field accordingly, document its range, and specify whether comparisons across topics or model versions are meaningful. The topic classification evaluation guide develops the practical checks behind those choices.

Make uncertainty and empty results explicit

An empty topic list can mean several things: no supported subject, unreadable input, a missing vocabulary, or an interrupted classifier. Give these outcomes distinct states. A successful classification with no relevant labels should remain distinguishable from a request that never reached a usable decision. Otherwise, downstream reports may count operational failures as evidence that a subject disappeared.

Consider a brief consisting only of “More details soon.” A sensible response might be an insufficient-context state with an empty assignment array. For an unrelated but complete document, the response might instead be outside-scope. These states lead to different actions: retrieve more content for the first, and accept that the vocabulary does not cover the second.

Write handling rules for conflicting evidence too. A headline may emphasize a sports event while the body primarily discusses a sponsorship dispute. Your application can prefer full-text evidence, request editorial review, or expose separate classifications for separate fields. The important design choice is to make that behavior intentional and reproducible.

Design versions around meaning

Keep the interface version separate from the taxonomy version. Changing an optional display field is different from splitting a broad topic into two narrower concepts. The response shape may remain identical while the classification meaning changes substantially. A client that only checks the endpoint version would miss that change unless the vocabulary release is recorded.

For a renamed topic, retain the identifier when the definition stays equivalent. For a split, create explicit successor concepts and preserve the old record for historical interpretation. An old assignment to “Urban mobility” cannot automatically reveal whether the document belongs to cycling infrastructure or public transport. Mark a suggested migration as a suggestion when evidence must be reviewed again.

Publish small migration examples alongside each release. Show an unchanged document before and after a label edit, then show a document that needs reassessment after a split. This approach is especially useful for news topic workflows, where archive continuity and today's editorial vocabulary must coexist.

Specify behavior at the edges

Document what happens when the same document revision is submitted twice. Decide whether the operation returns a stored result or creates a new classification attempt. Either approach can be useful, but consumers need an attempt identifier when results may differ. Preserve enough context to distinguish an intentional reclassification from an accidental duplicate delivery.

For batch inputs, return an outcome for each submitted item. One malformed document should not make every other result ambiguous. Keep the caller's document identifier attached to errors as well as successes, and document whether response order follows input order. A client should be able to reconcile a batch without guessing which omitted item failed.

Also define text limits, accepted languages, and normalization rules. If your application removes boilerplate or truncates a long article, record what happened. A topic assignment based on the opening paragraphs deserves a different review from one based on the complete text. Store only the evidence needed for your workflow, and choose retention deliberately.

Review the contract with a consumer

Walk through three sample documents with the person building the next interface. Include one straightforward match, one ambiguous case, and one valid empty result. Ask them to explain what their screen would show and what action follows. Missing fields often become obvious when someone tries to render a useful review card.

Then inspect a historical result after a vocabulary change. Can the consumer still recover the original definition and document revision? Can they distinguish a machine suggestion from an editor's accepted assignment? If structured generation will produce your responses, use the LLM extraction contract guide to separate parsing checks from topic evidence.

Conclusion: make the decision inspectable

A strong contract connects stable concepts to specific documents and explains the limits of each assignment. Start with identifiers, definitions, evidence, and explicit outcomes. Add complexity only when a consumer can explain why it is needed. The result is easier to integrate, easier to review, and easier to revise when your content or vocabulary grows.

TopicsAPI.com

Practical topic API guides for developers and publishers.

Topics API: A useful Topics API begins with a shared vocabulary: stable identifiers, readable labels, and explicit relationships. Learn how to design topic records that developers can integrate and editors can understand.

AI Topics API: AI topic classification connects unstructured text to a defined vocabulary. Explore label design, representative evaluation sets, and review policies that keep the output useful when the text gets complicated.

News Topics API: News topic systems connect individual stories to a broader editorial vocabulary. Design categories that support discovery while keeping events, people, places, and corrections visible in their own fields.

LLM Topics API: A language model can propose topic labels, but an application needs a predictable structure and a way to check the meaning. Plan LLM topic extraction around allowed concepts, evidence, and explicit failure handling.

Prompts Topics API: Topic prompts work best when they define the task, explain category boundaries, and show what to do with ambiguity. Build prompts that you can version, test, and discuss with the people who use the results.

AI Model Topics API: A model catalog needs more than a name and a broad AI label. Organize model-related content by task, input, output, evaluation context, and documented limitations so readers can compare the right things.

Prediction Topics API: Prediction-related content becomes easier to compare when its subject, horizon, assumptions, and resolution rules are explicit. Design records that preserve those distinctions without turning a topic label into a forecast.

Finance Topics API: Financial documents mix subjects, organizations, instruments, and reporting periods. A careful topic model keeps those concepts distinct and preserves the source context needed to interpret each document.

AGI Topics API: AGI discussions can combine definitions, evaluation proposals, capability claims, and speculation. A useful taxonomy keeps those different kinds of statements visible and connected to their original context.

Country Topics API: A country mention can describe a location, an institution, a source, or the audience of a story. Design geographic metadata that captures the role of the place and preserves the context of the original text.

Social Media Topics API: Social content is short, repetitive, and often missing context. Build topic analysis around permitted data, documented sampling, careful deduplication, and clear distinctions between what was observed and what was inferred.

Political Topics API: Political content needs careful distinctions between policy, institutions, people, statements, and events. Create subject metadata that supports informed reading while keeping viewpoints and factual claims connected to their sources.

Topics API Guide

Topics API

A useful Topics API begins with a shared vocabulary: stable identifiers, readable labels, and explicit relationships. Learn how to design topic records that developers can integrate and editors can understand.

Start with the job your labels must do

A topic is a reusable concept that helps people organize or retrieve content. A tag is often a lightweight label attached during publishing. An entity identifies something specific, such as an organization or a place. Treating these as separate fields makes it easier to ask whether a story is about energy policy, mentions an energy company, or belongs in an editor’s weekend collection.

Before choosing a model or endpoint, write down the decisions the labels will support. A navigation menu may need one broad category. An archive may need several detailed subjects. A reporting workflow may need a human review queue. The same document can serve all three purposes without forcing every purpose into one label.

Design a small, durable contract

Give each concept a stable ID and keep the display label separate. Include a short definition, an optional parent, a taxonomy version, and the language of the label. An identifier should continue to mean the same concept when you correct a spelling or translate its name.

Keep the topic catalog separate from document assignments. The catalog defines what a concept means; an assignment records why that concept was applied to one piece of content. This separation lets you improve a classifier without silently rewriting the vocabulary underneath existing integrations.

Make uncertainty useful

A classifier can return an allowed label while misunderstanding the text. Decide how your application will represent uncertainty, insufficient context, and content outside the taxonomy. An explicit review state is more useful than a plausible but unsupported label.

Use a small collection of representative documents to inspect mistakes. Include short items, overlapping categories, multiple languages, and examples that should receive no label. Review the failure cases with the people who use the taxonomy; a technically valid response is only the beginning of a useful result.

Plan changes before the first integration

Publish a change policy for renamed labels, merged concepts, and retired IDs. Keep a mapping when you migrate from one taxonomy version to another, and distinguish exact replacements from approximate matches. A consuming application should be able to explain why an old record displays differently.

Begin with a compact vocabulary and expand when a repeated reader need warrants it. Every new label creates work: definitions, examples, review rules, and migration decisions. A smaller taxonomy that people understand often makes a stronger foundation than a large list with overlapping meanings.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for Topics API
FIELD	PURPOSE
`topic_id`	Stable concept identity
`label`	Human-readable name
`taxonomy_version`	Vocabulary used for interpretation
`review_status`	Whether the assignment needs review

{
  "document_id": "example-article",
  "taxonomy_version": "demo-v1",
  "topics": [
    {
      "topic_id": "ai.language-models",
      "label": "Language models"
    }
  ],
  "review_status": "needs_review"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about Topics API

Is this Google’s browser Topics API?

TopicsAPI.com focuses on content classification and topic-data design. Google’s browser advertising Topics API is a separate technology; the shared wording does not imply affiliation or integration.

Do the examples call a hosted API?

The examples are illustrative records you can study and adapt. Read the implementation guides to plan your own taxonomy, validation, and review workflow.

Should I use one topic or several?

Choose the smallest number that serves the reading or retrieval task. Set clear rules for a primary topic and any secondary topics, then evaluate those rules on real documents.

AI Topics API Guide

AI Topics API

AI topic classification connects unstructured text to a defined vocabulary. Explore label design, representative evaluation sets, and review policies that keep the output useful when the text gets complicated.

Define the classification task first

Decide what the system should classify: a sentence, a whole article, a transcript segment, or a collection of documents. The choice changes what context is available. A short headline may imply a subject that the full article only mentions in passing.

Write a definition and several contrasting examples for each category. Include a boundary example where the correct decision is debatable. Those cases often expose an unclear policy before any model is involved. Editors and developers should be able to explain the same label in similar terms.

Choose a baseline you can inspect

A simple keyword rule can be a useful starting point for a narrow, well-defined category. A trained classifier or language model may recognize wording that the rule misses, but it also introduces new failure modes. Compare approaches on the same documents and the same label definitions.

Record the model or rule version separately from the taxonomy version. A change to the decision engine is different from a change to the meaning of a topic. Keeping both visible helps you investigate whether a shift in assignments reflects content, model behavior, or editorial policy.

Measure the errors that matter

Precision asks how many assigned labels are correct; recall asks how many relevant labels were found. Those questions serve different workflows. An automatic public label may need stricter review than a suggestion shown privately to an editor.

Inspect results by topic, language, document length, and source type. A single average can hide a category that never receives a correct assignment. Keep a held-out set that is separate from examples used to adjust prompts or rules, and record the decisions made when reviewers disagree.

Build a review path into the record

Let uncertain documents move into an explicit review state. Preserve the text excerpt that supports an assignment when your data permissions permit it, and make the reason easy to inspect. An explanation should point to the document rather than simply repeat the category name.

Monitor a sample of accepted assignments after launch. Changes in writing style or subject matter can make earlier evaluation results less representative. Use those observations to update the review set deliberately, with a record of what changed and why.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for AI Topics API
FIELD	PURPOSE
`model_version`	Decision engine identifier
`topic_ids`	Allowed assigned concepts
`evidence`	Supporting document excerpt
`review_status`	Accepted or awaiting review

{
  "document_id": "example-brief",
  "model_version": "example-classifier-v1",
  "topic_ids": [
    "ai.evaluation"
  ],
  "evidence": "The team compared label errors.",
  "review_status": "needs_review"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about AI Topics API

Does a confidence score prove correctness?

No. Treat any score according to how it was defined and evaluated. Compare scores with observed outcomes before using them to automate a decision.

Can one article have multiple AI topics?

Yes, if the task calls for multiple subjects. Define how secondary labels are selected and avoid assigning every concept that receives a brief mention.

What should go into a test set?

Use representative and difficult examples, including overlapping labels, unfamiliar wording, multilingual text, and documents outside the vocabulary.

News Topics API Guide

News Topics API

News topic systems connect individual stories to a broader editorial vocabulary. Design categories that support discovery while keeping events, people, places, and corrections visible in their own fields.

Separate subjects from developing events

A subject such as public transport can remain useful for years. A particular rail disruption is an event with a time and place. Keep both, but give them different roles. A reader looking for transport policy should not have to navigate an unstructured list of individual incidents.

Use a primary topic to explain the central editorial subject and secondary topics for substantial additional coverage. An incidental mention of a company or politician should not automatically determine the story’s category. Write this distinction into the newsroom’s examples.

Give editors a vocabulary they can maintain

Start with a clear hierarchy and readable scope notes. A broad category can support navigation while a narrower concept improves retrieval. Avoid creating a new permanent topic every time a headline introduces a new phrase.

For established news vocabularies, examine the concept definitions and mappings before adopting labels. Check the applicable terms and your editorial needs. A taxonomy can provide a common starting point, but the way your publication assigns and displays topics still needs its own policy.

Keep publication and event time separate

A report can describe an event that happened before publication, and it may receive a correction afterward. Preserve these distinctions so an archive does not mistake an updated story for a newly occurring event. Use an explicit time zone when recording a timestamp.

Store source identity and story identity as separate fields. Syndicated copies, translations, and follow-up reports may share a subject without being the same document. A topic count should explain whether it measures documents, unique stories, or clusters of related coverage.

Design for corrections and retrieval

A correction can change a named entity, a location, or the central topic. Keep a version history for assignments when those changes affect reader-facing navigation or analytics. Make it possible to withdraw a label without losing the record of why it appeared.

Test retrieval with reader questions, not only isolated documents. Ask whether someone can find the background to a developing event, distinguish opinion from reporting when that metadata exists, and move from one article to a useful subject archive.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for News Topics API
FIELD	PURPOSE
`story_id`	Editorial document identity
`topic_ids`	Subject classifications
`event_time`	When the reported event occurred
`source_id`	Origin retained with the story

{
  "story_id": "example-report",
  "topic_ids": [
    "transport.rail",
    "public-policy"
  ],
  "place_ids": [
    "example-region"
  ],
  "source_id": "example-publisher",
  "content_type": "reporting"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about News Topics API

Is a news topic the same as a news event?

A topic describes a reusable subject. An event describes a particular occurrence. Keeping them separate supports both background reading and event coverage.

How many categories should a newsroom use?

Enough to serve real navigation and retrieval needs, with clear boundaries and ownership. Test a small working hierarchy before adding detailed branches.

How should syndicated stories be counted?

Define the unit you count. Preserve source records, but use a documented deduplication or clustering policy when a dashboard represents unique stories.

LLM Topics API Guide

LLM Topics API

A language model can propose topic labels, but an application needs a predictable structure and a way to check the meaning. Plan LLM topic extraction around allowed concepts, evidence, and explicit failure handling.

Constrain the vocabulary before the output

An open-ended request for topics may produce a different spelling or level of detail each time. That can be useful during exploration, but a stable application usually needs a defined set of concepts. Supply IDs, definitions, and the permitted assignment range, including zero when no topic fits.

Separate discovery from classification. Discovery helps you notice a subject missing from the vocabulary. Classification applies the vocabulary that already exists. A proposed new concept should enter an editorial review process rather than immediately becoming a production category.

Validate shape and meaning separately

A JSON contract can describe required fields, permitted values, and the expected type of each field. A valid object is easier for software to consume. It does not establish that the selected topic is justified by the source text.

Add semantic checks after structural validation. Confirm that the selected IDs exist in the intended taxonomy version, that quoted evidence appears in the document, and that the result follows your policy for primary and secondary subjects. Route failures to a clear review state.

Treat document text as untrusted input

The content being classified may contain instructions, quoted prompts, code, or deliberately misleading text. Keep the classification policy separate from the document and instruct the workflow to treat the document as evidence rather than as authority over the task.

A prompt is one part of a larger boundary. Use constrained output where available, limit the model’s privileges, and validate before downstream use. A topic labeling step rarely needs the ability to send messages, execute document instructions, or change unrelated records.

Make retries observable and bounded

If a response fails validation, capture the reason and use a limited retry policy. Avoid unlimited attempts that quietly increase cost or eventually accept a different interpretation. A reviewable failure is often more useful than a successful-looking record with unknown reliability.

Track model configuration, prompt version, and taxonomy version with each evaluation run. When an output changes, these details help you reproduce the conditions and decide whether the new behavior improves the actual reader or editor task.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for LLM Topics API
FIELD	PURPOSE
`taxonomy_version`	Permitted concept set
`topic_ids`	Validated concept identifiers
`evidence`	Source text supporting the choice
`status`	Classified, unknown, or review

{
  "taxonomy_version": "demo-v1",
  "topic_ids": [
    "publishing.metadata"
  ],
  "evidence": [
    "The archive uses structured subject labels."
  ],
  "status": "review"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about LLM Topics API

Does structured JSON prevent incorrect topics?

It helps control output structure. You still need evidence checks and evaluation to assess whether the labels fit the document.

Should the model create new topic names?

Allow that in a separate discovery workflow if it is useful. Keep a production classification vocabulary stable until proposed concepts are reviewed.

How should an unknown subject be represented?

Use an explicit unknown or review state, with enough context for a person to decide whether the vocabulary or the assignment should change.

Prompts Topics API Guide

Prompts Topics API

Topic prompts work best when they define the task, explain category boundaries, and show what to do with ambiguity. Build prompts that you can version, test, and discuss with the people who use the results.

State the decision in plain language

Start with the unit of content and the intended decision. For example: assign the central subject of an article from this vocabulary, and include a secondary subject only when it receives substantial coverage. That is more useful than asking for every topic that seems relevant.

Define what should happen when the document lacks enough context. The instruction should permit an unknown or review outcome. Forcing a choice may make every response appear complete while concealing uncertainty that matters to the editor.

Teach the boundaries with examples

Include a clear positive example, a clear negative example, and a borderline example for categories that are easily confused. Explain the reason for each assignment. A short, relevant contrast can reveal more about the intended task than a long list of unrelated demonstrations.

Keep examples separate from the evaluation set. If you repeatedly tune the prompt against the same documents, improved results may reflect familiarity with those examples. Reserve new documents for checking whether the rule transfers to unfamiliar writing.

Specify a compact output contract

Tell the system which fields to return and which values are permitted. IDs are usually safer integration keys than free-form display names. Put reasons or excerpts in separate fields so they cannot accidentally become new categories.

Avoid requesting elaborate reasoning when a short evidence excerpt serves the workflow. Give reviewers the information needed to inspect the assignment while keeping the record concise. The contract should fit the application’s decision, not the amount of text a model can produce.

Change one decision at a time

Version prompts and keep a short note of the behavior each revision is meant to improve. If you alter definitions, examples, output shape, and model configuration together, it becomes difficult to tell which change produced the result.

Review the new prompt on the same held-out set and inspect category-specific errors. A wording change that helps one topic may hurt another. Keep a rollback path and avoid assuming that a more detailed prompt is automatically a more reliable prompt.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for Prompts Topics API
FIELD	PURPOSE
`prompt_version`	Reproducible instruction set
`allowed_topic_ids`	Classification vocabulary
`max_topics`	Documented assignment limit
`unknown_policy`	How ambiguity is handled

{
  "task": "assign_central_subject",
  "prompt_version": "example-v1",
  "allowed_topic_ids": [
    "ai.models",
    "ai.evaluation"
  ],
  "max_topics": 2,
  "unknown_policy": "send_to_review"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about Prompts Topics API

How long should a classification prompt be?

Long enough to define the task and its difficult boundaries. Add instructions when they address observed errors, and remove duplicated wording that obscures the rule.

Can one prompt work across languages?

Treat that as a hypothesis to test. Use examples and reviewers appropriate to the languages in your content, and inspect results separately.

When should a prompt change become a new version?

Whenever the instructions, examples, or output contract can change assignments. Record the version so evaluations and production results remain interpretable.

AI Model Topics API Guide

AI Model Topics API

A model catalog needs more than a name and a broad AI label. Organize model-related content by task, input, output, evaluation context, and documented limitations so readers can compare the right things.

Use several dimensions instead of one ranking

A model can be described by its task, accepted inputs, produced outputs, and intended use. These dimensions answer different questions. A broad label such as language model does not say whether a particular deployment is suitable for a specific document workflow.

Keep the catalog’s topics separate from claims about an individual model. A page about image generation can carry that topic without asserting that every model mentioned supports it. Store the actual capability statement with its source and scope.

Attach claims to a version and a context

Model names can represent families, releases, or hosted aliases. Record the level you mean. If a capability was documented for a specific version, avoid silently transferring the claim to every member of the family.

Evaluation results also need context: task, dataset, language, configuration, and the conditions under which the work was tested. A score without those details is difficult to interpret. Use the topic vocabulary to support discovery, while preserving the original evaluation description.

Keep access and licensing out of assumptions

A topic label does not grant access to a model or establish rights to deploy it. Keep distribution method, usage conditions, and the date you checked them in separate metadata when they are relevant to the catalog.

Avoid translating broad marketing language into a verified technical feature. A claim such as general purpose may describe the provider’s positioning. A catalog should preserve attribution and distinguish an advertised capability from an independently assessed workflow.

Plan for updates and retired releases

A useful model knowledge base should explain what changes when a release is superseded. Keep older records available where they help interpret historical articles or evaluations. Deprecation of an identifier should not erase the context attached to past work.

Use the same discipline for emerging concepts such as AGI. Define whether an item is a research discussion, a benchmark proposal, or a capability claim. Readers should be able to distinguish the subject of an article from evidence about a deployed system.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for AI Model Topics API
FIELD	PURPOSE
`model_reference`	Named release or family
`task_topics`	Work the content discusses
`evidence_scope`	Conditions behind the claim
`source_checked`	When supporting material was reviewed

{
  "model_reference": "example-model-v1",
  "task_topics": [
    "text.classification"
  ],
  "input_modalities": [
    "text"
  ],
  "evidence_scope": "example evaluation only",
  "claim_status": "attributed"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about AI Model Topics API

Is a model taxonomy a leaderboard?

No. A taxonomy organizes concepts. A comparison needs separate evidence, comparable tasks, and a clear account of how results were measured.

Should hosted aliases be treated as fixed releases?

Record them as aliases when that is what they are. Preserve a resolved version when it is available and material to reproducibility.

Where do AGI discussions belong?

Use a clearly scoped research topic and retain the source’s definition. Do not convert a broad topic label into a statement that a system has general intelligence.

Prediction Topics API Guide

Prediction Topics API

Prediction-related content becomes easier to compare when its subject, horizon, assumptions, and resolution rules are explicit. Design records that preserve those distinctions without turning a topic label into a forecast.

Separate the subject from the forecast

A topic identifies what a prediction concerns, such as energy demand or election participation. The forecast itself is a statement about an outcome under specified conditions. Keeping those layers separate prevents a content classification from being mistaken for a prediction.

Write the question in terms that could be resolved consistently. Ambiguous subjects lead to ambiguous comparisons. Two articles can discuss the same broad topic while predicting different variables, time periods, or geographic areas.

Make time part of the meaning

Record when a forecast was made and the period it concerns. A short-term estimate and a longer scenario are different records even when the headline uses the same words. Preserve the original time zone and distinguish a deadline from an observation window.

Avoid updating old forecast records in place when assumptions change. Create a revision with an explicit relationship to the earlier record. Historical context matters when readers want to understand what was known at the time.

Preserve the form of uncertainty

A probability, an interval, a scenario, and a qualitative statement express different things. Store the representation explicitly rather than squeezing every prediction into one numeric field. A missing probability should remain missing, not become an invented number.

Keep assumptions beside the record: the population, observation method, data availability, and any conditions attached to the claim. A classification system can make these fields visible without claiming to verify the forecast’s accuracy.

Define how a result will be resolved

A prediction record should explain what would count as the outcome and which evidence would be used to assess it. Consider revisions to underlying observations and cases where no decisive result becomes available.

Use topics to group comparable questions, then check comparability before aggregating. Mixing different horizons or definitions can make a collection look informative while obscuring the actual claims. A useful archive supports inspection of the original statement and its context.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for Prediction Topics API
FIELD	PURPOSE
`question`	Precisely scoped subject
`made_at`	Time of the original statement
`horizon`	Period the statement concerns
`resolution_rule`	How an outcome is determined

{
  "topic_id": "energy.demand",
  "question": "Example demand scenario for a defined region",
  "horizon": "specified future period",
  "uncertainty_type": "scenario",
  "resolution_status": "unresolved"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about Prediction Topics API

Does a prediction topic provide a forecast?

A topic organizes content about a forecast subject. The actual forecast, its assumptions, and any assessment belong in separately attributed fields.

Should all forecasts use a probability field?

No. Preserve the original representation. A scenario or interval should not be converted into an invented point probability.

Can different prediction records be combined?

Only after checking that their outcome definitions, horizons, populations, and measurement rules are compatible for the intended comparison.

Finance Topics API Guide

Finance Topics API

Financial documents mix subjects, organizations, instruments, and reporting periods. A careful topic model keeps those concepts distinct and preserves the source context needed to interpret each document.

Keep topics separate from entities and values

Corporate reporting is a subject; a named issuer is an entity; a reported amount is a value with a unit and period. Each serves a different retrieval need. A document can mention several companies while being principally about one reporting issue.

Use separate fields for organizations, instruments, currencies, and topic concepts. Avoid guessing an identifier from an ambiguous name. If a symbol or abbreviation cannot be resolved from the source context, preserve the ambiguity for review.

Make the reporting period visible

A document’s publication date is not necessarily the period its figures describe. Retain both when they are available. A revised filing may refer to an earlier period, and a comparison may combine several periods within one document.

When extracting a statement, keep the relevant context with it. A number without its unit, period, and source can become misleading when displayed elsewhere. Topic classification should help retrieve the document without stripping away what the statement means.

Create labels around reading tasks

Useful subjects might include financial reporting, corporate actions, market structure, or disclosure methods. Define each according to the content people need to find. Avoid designing a vocabulary solely from whatever words appear frequently in headlines.

Separate article type from subject. Commentary, an official filing, and a general explainer can all concern the same topic. Preserving that distinction helps readers understand the kind of evidence they are viewing before they interpret the details.

Inspect high-consequence ambiguity

Review labels that could change how a sensitive statement is understood. A document may quote a forecast, discuss a risk factor, or describe a hypothetical scenario without asserting that the event occurred. Preserve attribution and conditional language.

The purpose of this guide is information architecture for financial content. Topic labels do not establish the accuracy of a financial statement or the suitability of an investment. Build a workflow that keeps the original source available and assigns an explicit review state when context is incomplete.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for Finance Topics API
FIELD	PURPOSE
`document_type`	Filing, commentary, or explainer
`entity_references`	Organizations or instruments mentioned
`reporting_period`	Period described by the content
`source_reference`	Origin of the statement

{
  "document_type": "example-report",
  "topic_ids": [
    "finance.reporting"
  ],
  "entity_references": [
    "example-issuer"
  ],
  "reporting_period": "example-period",
  "source_reference": "example-document"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about Finance Topics API

Are finance topics a market data feed?

This guide covers the organization of financial content. Price feeds, financial statements, and other data sources have their own contracts and provenance requirements.

Can a ticker symbol identify a company everywhere?

Do not assume that it can. Preserve the context needed to resolve an identifier and review ambiguous or incomplete references.

Should a risk discussion receive an event label?

Only if the source supports that event classification under your policy. A hypothetical risk and a reported occurrence should remain distinguishable.

AGI Topics API Guide

AGI Topics API

AGI discussions can combine definitions, evaluation proposals, capability claims, and speculation. A useful taxonomy keeps those different kinds of statements visible and connected to their original context.

Record the definition being used

When a source discusses artificial general intelligence, preserve the definition or framing that gives the claim meaning. A document may focus on breadth of tasks, transfer to unfamiliar problems, or a particular evaluation proposal. These are related discussions, but they are not automatically interchangeable.

Use topic labels to improve discovery across this literature. Keep the source’s wording and the catalog’s normalized concept separate. That allows a reader to find related material while still seeing how different authors frame the underlying question.

Distinguish a proposal from a result

A benchmark proposal describes a way to evaluate a system. A reported result describes performance under particular conditions. A capability claim interprets what that result might mean. Give these different record types so the interface does not flatten them into one assertion.

Retain task scope, test conditions, and the source of each claim. When information is unavailable, leave it unresolved. A polished catalog entry should not imply that a missing evaluation has been performed.

Topics such as reasoning, planning, adaptation, evaluation, and AI safety can help readers navigate research. Applying one of those labels to an article says what the article discusses; it does not establish that a model reliably demonstrates the property in every setting.

Create scope notes for ambiguous concepts and maintain examples that show how the labels are assigned. If two reviewers disagree, examine the definition before assuming the disagreement can be fixed by a different classifier.

Make attribution easy to inspect

A reader should be able to tell who made a claim, what evidence was presented, and whether the entry is describing a proposal, an observation, or an interpretation. Preserve those distinctions in summaries as well as detailed records.

Connect AGI discussions to model and evaluation guides when they share methods or terminology. Keep the navigation useful while resisting a universal score or label that compresses different research questions into an unsupported conclusion.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for AGI Topics API
FIELD	PURPOSE
`concept_definition`	Meaning used by the source
`statement_type`	Proposal, observation, or interpretation
`evaluation_scope`	Tasks and conditions described
`claim_attribution`	Where the statement originated

{
  "topic_id": "ai.general-intelligence",
  "statement_type": "research-discussion",
  "evaluation_scope": "source-defined tasks",
  "claim_attribution": "example-research-note",
  "review_status": "context_required"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about AGI Topics API

Does an AGI topic label confirm a capability?

No. It identifies the subject of content. A capability claim needs its own attribution, definition, and evidence.

How should competing definitions be handled?

Preserve the definition used by each source and relate it to the catalog’s concepts. Avoid silently treating different definitions as equivalent.

What belongs in an AGI research archive?

Clearly classified research discussions, evaluation proposals, reported results, and critiques, with enough context to tell those record types apart.

Country Topics API Guide

Country Topics API

A country mention can describe a location, an institution, a source, or the audience of a story. Design geographic metadata that captures the role of the place and preserves the context of the original text.

Ask what the place is doing in the story

A report may be published in one country, describe an event in another, and discuss policy affecting several others. A single country field cannot explain those relationships. Define the role of each place reference before deciding how it should appear in navigation.

Keep source location, event location, and geographic subject separate when they matter to the reader. Do not infer the audience or identity of the author from the language alone. A multilingual document can discuss a place without being produced there.

Use identifiers with a stated scheme

A normalized geographic identifier helps join records, but the scheme and version should remain visible. Preserve the source label alongside the normalized value, especially where names differ by language or editorial style.

An identifier is a technical reference, not a substitute for explaining the geographic scope. Regions, territories, cities, and cross-border areas may need different levels of detail. Choose the resolution that serves the retrieval task rather than forcing every reference into a country bucket.

Preserve ambiguity and historical context

A name can refer to more than one place, and place references can change meaning across time or context. Use surrounding evidence to resolve them and keep an explicit review state when the text does not support a confident choice.

When the document describes a historical event, retain the event’s context and the source’s wording. Avoid silently rewriting an old record to match a current label if doing so changes its meaning. A mapping can support modern retrieval while preserving the original account.

Evaluate across languages and sources

Test geographic assignments with local-language examples and people familiar with the relevant context. Check whether short names, abbreviations, or translated institutions create systematic errors. A global average can hide a poor result for a smaller language collection.

Design country archives around useful questions. Readers may want regional background, cross-border comparisons, or reporting about a specific event. Make the role and granularity of geographic labels visible so the archive explains why an article appears there.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for Country Topics API
FIELD	PURPOSE
`place_reference`	Normalized geographic identity
`place_role`	Event location, subject, or source
`source_label`	Original name in the document
`language`	Language of the recorded wording

{
  "place_reference": "example-region",
  "place_role": "event_location",
  "source_label": "Example Region",
  "language": "en",
  "topic_ids": [
    "public-policy"
  ]
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about Country Topics API

Is the country of a publisher the country of a story?

Not necessarily. Record the publisher’s location separately from the event location and the geographic subject of the content.

Should the original place name be retained?

Yes, when your data contract allows it. The original wording helps reviewers understand how a normalized reference was derived.

How should cross-border stories be labeled?

Allow multiple place references with explicit roles. Explain the primary geographic focus only when the source and your editorial policy support it.

Social Media Topics API Guide

Social Media Topics API

Social content is short, repetitive, and often missing context. Build topic analysis around permitted data, documented sampling, careful deduplication, and clear distinctions between what was observed and what was inferred.

Define the collection before counting topics

Write down which sources, languages, time windows, and collection methods your dataset covers. A count only describes the material you observed. It should not quietly become a claim about all users, an entire platform, or public opinion.

Keep a record of missing or unavailable content when that affects interpretation. A change in collection coverage can produce a change in topic counts even when the underlying discussion has not changed in the same way.

Distinguish duplicates from shared subjects

Identical reposts, lightly edited copies, and original posts about the same event are different relationships. Decide whether the workflow counts individual posts, unique texts, or clusters. Preserve the original records even when the display groups them.

A repeated phrase can be evidence of copying without proving coordination or intent. Keep the classification close to what the data supports. Topic grouping should help a reader inspect the material, not attribute motives that the record cannot establish.

Classify with the context available

A hashtag may be ironic, unrelated, or too broad to explain a post. Use surrounding text and linked context only when you have appropriate access and a clear reason to include it. Mark the limits when the available text is insufficient.

Separate topic, sentiment, and stance into different tasks. A post about a policy is not necessarily favorable or unfavorable toward it. Avoid inferring a person’s political belief or sensitive identity from a broad content label.

Make trend summaries auditable

If you display a topic’s change over time, retain the denominator, window, and deduplication policy used for each measurement. Compare like with like. A large increase from a tiny baseline can be less informative than the headline suggests.

Respect the conditions under which the underlying material can be used, retained, and displayed. Topic metadata does not automatically remove obligations attached to source content. Keep deletion and correction handling connected to the records that depend on that content.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for Social Media Topics API
FIELD	PURPOSE
`observation_window`	Period of the collected material
`collection_scope`	What the sample includes
`cluster_id`	Documented grouping of related records
`count_unit`	Posts, unique texts, or clusters

{
  "topic_id": "publishing.community",
  "observation_window": "example-window",
  "collection_scope": "permitted sample",
  "count_unit": "unique_texts",
  "deduplication_policy": "example-v1"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about Social Media Topics API

Does a hashtag prove the topic of a post?

No. Treat it as one piece of evidence and inspect the surrounding content, including ambiguity and irony.

Can post counts represent public opinion?

A collection describes the material it includes. Do not extend its meaning beyond the documented sample and analysis method.

Should repeated posts be deleted?

Keep a documented source record where permitted. Group or exclude duplicates for a particular analysis without losing the relationship needed to explain the result.

Political Topics API Guide

Political Topics API

Political content needs careful distinctions between policy, institutions, people, statements, and events. Create subject metadata that supports informed reading while keeping viewpoints and factual claims connected to their sources.

Build categories around public subjects

Policy areas, institutional processes, and civic events can provide useful subject categories. Define them in terms of what a document discusses. A person’s name or party reference should not automatically determine a policy topic.

Keep topic, genre, and viewpoint separate. Reporting on a proposal, an opinion piece about it, and the proposal itself can share a subject while serving different purposes. Preserve enough metadata for readers to recognize which kind of source they are viewing.

Attach statements to their speakers and context

A document can report a claim without endorsing it. When summarizing or extracting a statement, retain who made it and the context in which it appeared. A classifier should not turn quoted rhetoric into an unattributed description of reality.

Avoid inferring political affiliation or beliefs about an individual from a document-level topic. The goal is to organize public content for retrieval. Keep records about the subject of a text separate from any unsupported profile of the person associated with it.

Make jurisdiction and time explicit

The same institutional term can mean different things across countries and systems. Preserve jurisdiction, relevant dates, and the role of a named institution when the source provides them. Link to geographic context rather than relying on a universal label alone.

Policy proposals also move through stages. A discussion, introduced measure, enacted decision, and implementation update are different events. Avoid a taxonomy that makes every mention look like a completed action.

Use transparent review rules

Document how labels are assigned and how corrections are handled. Test contentious or ambiguous examples with reviewers who can distinguish subject classification from agreement or disagreement with the content. The aim is a consistent editorial rule, not a desired political conclusion.

A useful political archive helps readers inspect original evidence, follow a policy over time, and compare coverage with its context intact. Keep the vocabulary open to correction and avoid promising neutrality as a property that can be established by a topic label alone.

An illustrative record

This example highlights fields worth discussing when you design your own contract. Define their meanings, allowed values, and review rules before an application relies on them.

Example fields for Political Topics API
FIELD	PURPOSE
`policy_topic`	Public subject under discussion
`jurisdiction`	Relevant institutional context
`statement_attribution`	Who made the recorded claim
`process_stage`	Proposal, decision, or implementation

{
  "policy_topic": "transport.policy",
  "jurisdiction": "example-jurisdiction",
  "statement_attribution": "example-public-record",
  "process_stage": "proposal",
  "review_status": "context_checked"
}

Illustrative schema and example values; adapt them to your data and review process.

Questions about Political Topics API

Is topic classification the same as detecting political bias?

No. A subject label identifies what content discusses. Assessing framing or viewpoint is a separate task with its own definitions and evidence.

Should a quoted claim become a topic fact?

Retain its attribution and context. The presence of a statement in a document does not establish that the statement is true.

How should policy stages be represented?

Use explicit stages supported by the source, with dates and jurisdiction. Keep a proposal distinguishable from an adopted or implemented decision.

Topics API Lab

Ten practical articles on topic design and classification.

Preserve Country and Political Context in Topic Data — A place name, an institution, and a political subject play different roles in a document. Preserve those distinctions with sourced entity relationships, multilingual evidence, and review rules that make geographic context inspectable.

Design Prediction Topic Contracts Without Inventing Forecasts — Prediction coverage needs careful boundaries between the subject, the statement, and its time horizon. Build a document contract that preserves uncertainty and provenance without presenting extracted labels as forecasts or probabilities.

Prompt design for topic labels: definitions, evidence, and edge cases — A strong labeling prompt describes a decision that another editor could follow. Learn to separate instructions from source text, choose examples that reveal topic boundaries, handle uncertainty, and evaluate changes against a stable review set.

Separate Social Topic Signals from Repeated Posts — Repeated posts can make one story look like many independent signals. Learn how to separate activities, content objects, and story clusters, then report observed topic patterns with clear sampling limits and evidence.

LLM topic extraction: a JSON contract you can actually validate — Valid JSON is the first check in a longer workflow. Design topic extraction around an allowed vocabulary, verifiable evidence, explicit outcomes, document revisions, and validation that connects a generated label to its source.

Build an AI Model Taxonomy That Keeps Claims in Context — Organize model coverage by task, modality, and evidence. This practical taxonomy separates the model being discussed from the system using it, while keeping broad capability claims attached to their sources and evaluation scope.

Evaluate AI topic classification before you trust the labels — A useful evaluation starts with the decisions a label will drive. Build a representative review set, calculate topic-level metrics, inspect disagreements, and compare revisions without hiding costly mistakes in a single average.

Classify Financial Documents Without Losing Entity and Time Context — Financial document labels need more than company names. Separate the document type, entities, reporting periods, and evidence so readers can find relevant material without confusing topic classification with a financial conclusion.

Build a news topic taxonomy that survives the next news cycle — News changes quickly, but a useful subject vocabulary needs continuity. Learn how to separate topics from sections and entities, handle evolving stories, map external vocabularies, and give editors clear rules for everyday classification.

Topics API design: IDs, labels, and useful JSON contracts — Build a topics API contract that readers, editors, and developers can understand. Explore stable identifiers, evidence, uncertainty, document revisions, and vocabulary changes through practical examples for content classification.

API Foundations Articles

LAB CATEGORY

API FoundationsThe contracts behind useful topic data.

Start with concept identity, clear field definitions, and a vocabulary that can evolve without breaking the records that depend on it. These guides explain how to separate topics from tags and entities, structure assignments, and make integration decisions visible. Read them before choosing a classifier or building a large catalog.

1 ARTICLE IN THIS COLLECTION

Trace one classification from source to consumer

Use the schema guide to follow a single document through a proposed integration. Identify the concept record, the assignment, and the document revision separately. Then ask what a consuming screen should show when the label changes, the evidence disappears, or the article receives an update. These cases reveal whether an identifier preserves meaning or merely preserves a convenient string.

Before adding fields, write the decision each field supports. Can a client distinguish an unsupported subject from an interrupted request? Can it recover the vocabulary definition used for an older assignment? Compare the foundations article with the prediction contract guide when a record includes statements and time horizons. Leave with a small field dictionary, three representative response examples, and an explicit rule for reviewing changed meanings.

API FoundationsMar 19, 2024

Topics API design: IDs, labels, and useful JSON contracts

Build a topics API contract that readers, editors, and developers can understand. Explore stable identifiers, evidence, uncertainty, document revisions, and vocabulary changes through practical examples for content classification.

Read the article

Connect the ideas.

Follow the core topic guides for examples, field definitions, and related reading.

Topics API foundations All Lab articles

AI & LLMs Articles

LAB CATEGORY

AI & LLMsFrom language to labels you can inspect.

Explore classification, prompts, structured output, and model metadata as parts of one reviewable workflow. The focus is practical: define the task, preserve evidence, validate the response, and evaluate errors on representative documents. Use these articles together to understand where a model can assist and where a clearer contract or editorial decision is needed.

4 ARTICLES IN THIS COLLECTION

Read the workflow in the order you will test it

Begin with the evaluation article and define what a correct label would allow your application to do. Choose difficult documents before choosing the wording of a prompt. Next, read the prompt and JSON extraction guides together: one describes the decision rule, while the other describes the checks that make its output usable. Ask whether every uncertain result has a clear next step.

Use the model taxonomy article when your archive also discusses AI systems themselves. Which claims belong to a named model version, and which depend on retrieval, preprocessing, or an application configuration? Keep that distinction in your review notes. A practical reading outcome is an evaluation sheet linking each observed error to the component that could fix it: vocabulary, prompt, source preparation, validation, or editorial review.

AI & LLMsJan 22, 2026

Prompt design for topic labels: definitions, evidence, and edge cases

A strong labeling prompt describes a decision that another editor could follow. Learn to separate instructions from source text, choose examples that reveal topic boundaries, handle uncertainty, and evaluate changes against a stable review set.

Read the article

AI & LLMsSep 4, 2025

LLM topic extraction: a JSON contract you can actually validate

Valid JSON is the first check in a longer workflow. Design topic extraction around an allowed vocabulary, verifiable evidence, explicit outcomes, document revisions, and validation that connects a generated label to its source.

Read the article

AI & LLMsMay 16, 2025

Build an AI Model Taxonomy That Keeps Claims in Context

Organize model coverage by task, modality, and evidence. This practical taxonomy separates the model being discussed from the system using it, while keeping broad capability claims attached to their sources and evaluation scope.

Read the article

AI & LLMsFeb 12, 2025

Evaluate AI topic classification before you trust the labels

A useful evaluation starts with the decisions a label will drive. Build a representative review set, calculate topic-level metrics, inspect disagreements, and compare revisions without hiding costly mistakes in a single average.

Read the article

Connect the ideas.

Follow the core topic guides for examples, field definitions, and related reading.

Topics API foundations All Lab articles

News & Publishing Articles

LAB CATEGORY

News & PublishingKeep the story and its context together.

Subject metadata helps readers move from an individual story to the wider reporting around it. These articles cover editorial taxonomies, repeated social content, provenance, and the distinction between a reusable subject and a developing event. Build archives that remain useful when stories are corrected, sources overlap, or a topic changes over time.

2 ARTICLES IN THIS COLLECTION

Test the archive with a developing story

Choose a hypothetical story with an initial brief, a fuller report, a correction, and several shared copies. Read the newsroom taxonomy guide to decide which lasting subjects connect those records. Then read the social deduplication article to decide which items represent separate documents, repeated text, or the same specific event. Ask what readers would lose if each relationship were reduced to one tag.

Sketch the resulting subject page before expanding the vocabulary. Which version deserves the prominent position? Where should a correction appear? Would independent reporting remain discoverable beside repeated copies? Use these questions to write an editorial display policy as well as an annotation policy. The useful outcome is a small collection that preserves continuity while making new information and meaningful differences easy to find.

News & PublishingNov 25, 2025

Separate Social Topic Signals from Repeated Posts

Repeated posts can make one story look like many independent signals. Learn how to separate activities, content objects, and story clusters, then report observed topic patterns with clear sampling limits and evidence.

Read the article

News & PublishingAug 27, 2024

Build a news topic taxonomy that survives the next news cycle

News changes quickly, but a useful subject vocabulary needs continuity. Learn how to separate topics from sections and entities, handle evolving stories, map external vocabularies, and give editors clear rules for everyday classification.

Read the article

Connect the ideas.

Follow the core topic guides for examples, field definitions, and related reading.

Topics API foundations All Lab articles

Applied Topics Articles

LAB CATEGORY

Applied TopicsUse the right context for each domain.

Topic design changes when the content includes forecasts, financial documents, countries, or political institutions. These guides show how to keep time, place, attribution, and uncertainty attached to the record. Read them when a generic category list would hide the distinctions your readers or applications need to understand.

3 ARTICLES IN THIS COLLECTION

Find the context a generic label would omit

Read the domain closest to your source material first, then compare its essential fields with another domain. Financial coverage needs reporting periods and entity relationships. Future-oriented statements need attribution, conditions, and a supported horizon. Geographic and political coverage needs place roles and institutional context. Ask which of those distinctions would disappear if your interface displayed only a subject and a date.

Work through one ambiguous record from each relevant guide. Could a company example become an apparent financial observation? Could a stated target become an apparent forecast? Could a publisher's location become an apparent event location? Write the missing context beside each mistake and decide whether it belongs in a structured field, a source passage, or a review note. This exercise produces a practical checklist for adapting a general taxonomy to a specific reading task.

Applied TopicsAug 18, 2026

Preserve Country and Political Context in Topic Data

A place name, an institution, and a political subject play different roles in a document. Preserve those distinctions with sourced entity relationships, multilingual evidence, and review rules that make geographic context inspectable.

Read the article

Applied TopicsJun 9, 2026

Design Prediction Topic Contracts Without Inventing Forecasts

Prediction coverage needs careful boundaries between the subject, the statement, and its time horizon. Build a document contract that preserves uncertainty and provenance without presenting extracted labels as forecasts or probabilities.

Read the article

Applied TopicsNov 7, 2024

Classify Financial Documents Without Losing Entity and Time Context

Financial document labels need more than company names. Separate the document type, entities, reporting periods, and evidence so readers can find relevant material without confusing topic classification with a financial conclusion.

Read the article

Connect the ideas.

Follow the core topic guides for examples, field definitions, and related reading.

Topics API foundations All Lab articles

API design Guides & Articles

LAB TOPIC / API DESIGN

API designCreate clear contracts for topic records.

API design connects your vocabulary to the software that uses it. Explore stable IDs, predictable fields, versioning, and explicit failure states. Start with the foundations guide, then compare how different domains adapt the same contract to their own context.

4 CONNECTED ARTICLES

Turn the reading into a consumer agreement

Imagine two teams exchanging topic assignments without sharing the same code. Use the design articles to decide which values both teams must interpret identically: concept identifiers, document revisions, vocabulary releases, and result states. Ask how the receiving team would reconcile a partially successful batch or distinguish a deliberate reclassification from a duplicate request. Document those answers before inventing additional endpoints.

Then review the contract through a change scenario. A display label is corrected, a concept is split, or an input document is replaced. Which change preserves the old assignment, and which needs new evidence? The schema and domain guides offer concrete examples for that discussion. Finish with one readable success record, one valid empty result, and one review case, each paired with the action its consumer should take.

Applied TopicsJun 9, 2026

Design Prediction Topic Contracts Without Inventing Forecasts

Read the article

AI & LLMsSep 4, 2025

Explore another practice.

Taxonomy Guides & Articles

LAB TOPIC / TAXONOMY

TaxonomyMake the vocabulary worth maintaining.

A taxonomy gives topic labels definitions and relationships. These articles discuss how to set boundaries, distinguish broad from narrow concepts, and avoid overlapping categories. Use the examples to write scope notes and decide when a new concept deserves a place in your catalog.

6 CONNECTED ARTICLES

Decide whether a new label deserves to exist

Bring a proposed topic to these articles with three documents: an obvious match, an obvious exclusion, and a difficult neighbor. Write the definition that separates them. Could an editor apply that definition without knowing your preferred answer? If two existing concepts already describe the distinction, consider a relationship or a second classification dimension before adding another permanent node to the hierarchy.

Compare the newsroom and AI model examples to choose between a tree and independent dimensions. Does the reader need a broader subject, a task, a modality, an institution, or an event collection? Those questions lead to different structures. Review aliases and external mappings with the same care: similar wording does not settle equivalent meaning. Leave with a scope note and a specific retrieval question that justifies each proposed concept.

AI & LLMsJan 22, 2026

Prompt design for topic labels: definitions, evidence, and edge cases

Read the article

AI & LLMsMay 16, 2025

Build an AI Model Taxonomy That Keeps Claims in Context

Read the article

AI & LLMsFeb 12, 2025

Evaluate AI topic classification before you trust the labels

Read the article

Applied TopicsNov 7, 2024

Classify Financial Documents Without Losing Entity and Time Context

Read the article

News & PublishingAug 27, 2024

Build a news topic taxonomy that survives the next news cycle

Read the article

API FoundationsMar 19, 2024

Topics API design: IDs, labels, and useful JSON contracts

Read the article

Explore another practice.

Evaluation Guides & Articles

LAB TOPIC / EVALUATION

EvaluationCheck the decisions behind the labels.

Evaluation should show where assignments help and where they mislead. Read about representative examples, category-specific errors, review policies, and the conditions behind reported results. Keep the test set and the intended user task visible whenever you compare approaches.

4 CONNECTED ARTICLES

Choose the mistake your review must expose

Start by naming an error that would materially change the reader's experience. An unsupported primary label can misplace an article; a false duplicate merge can hide a correction; an incorrect entity link can contaminate a company archive. Use that error to select representative cases and review criteria. Ask what evidence would convince you that a revision improved the intended task.

As you read, separate the reference decision from the model's suggestion and the reported percentage from its denominator. Can another reviewer reconstruct why an example was labeled? Are repeated versions of one story dominating the sample? Keep a stable comparison set for regression review, and reserve unfamiliar material for later evaluation. The practical deliverable is a release note that names improved cases, remaining failures, and the limits of the tested collection.

AI & LLMsJan 22, 2026

Prompt design for topic labels: definitions, evidence, and edge cases

Read the article

News & PublishingNov 25, 2025

Separate Social Topic Signals from Repeated Posts

Read the article

AI & LLMsMay 16, 2025

Build an AI Model Taxonomy That Keeps Claims in Context

Read the article

AI & LLMsFeb 12, 2025

Evaluate AI topic classification before you trust the labels

Read the article

Explore another practice.

Structured output Guides & Articles

LAB TOPIC / STRUCTURED OUTPUT

Structured outputTurn model responses into inspectable records.

Structured output makes a response easier to validate and consume. It still needs meaningful fields, allowed values, evidence checks, and a plan for unknown subjects. These guides connect JSON contracts with the editorial and engineering decisions that give the data meaning.

3 CONNECTED ARTICLES

Challenge a valid-looking response

Take a proposed JSON result and try to reject it for a meaningful reason. The syntax may be valid while the topic identifier is unknown, the evidence is blank, or the document revision is stale. Use the extraction and contract guides to assign each check to the right stage. Ask which failures a schema can detect and which require the vocabulary, source text, or editorial policy.

Next, inspect the uncertain cases. Is an absent value different from an unreadable source passage? Does a prediction record preserve the speaker's wording when a horizon cannot be normalized? Does an empty assignment list have an understandable outcome? Write examples that preserve these distinctions without unnecessary fields. Your resulting contract should make acceptance explainable and make rejection specific enough that a client can choose a useful next action.

Applied TopicsJun 9, 2026

Design Prediction Topic Contracts Without Inventing Forecasts

Read the article

AI & LLMsJan 22, 2026

Prompt design for topic labels: definitions, evidence, and edge cases

Read the article

AI & LLMsSep 4, 2025

LLM topic extraction: a JSON contract you can actually validate

Read the article

Explore another practice.

Editorial workflows Guides & Articles

LAB TOPIC / EDITORIAL WORKFLOWS

Editorial workflowsSupport the people maintaining the archive.

Editorial workflows connect automated suggestions with human judgment, corrections, and publishing needs. Explore topic assignment, story grouping, source context, and review queues. A useful system should help an editor understand and change a label without losing the record of why it was applied.

2 CONNECTED ARTICLES

Follow a correction through the publication

Use these articles to walk through a correction that changes a story's subject or location. Identify who reviews the new evidence, which assignment is replaced, and which subject pages should update. Then check the repeated copies and translated versions connected to the original. Ask whether the publishing workflow can display the current account while retaining enough history to explain an earlier label.

Read the taxonomy and deduplication guides together when choosing what an editor sees in a queue. A suggested topic, a possible duplicate, and a proposed story-cluster merge require different decisions. Show the evidence and the consequence of accepting each suggestion. A useful outcome is a short review checklist with an owner, an accepted state, and a correction path for each type of change, supported by examples from your own publishing practice.

News & PublishingNov 25, 2025

Separate Social Topic Signals from Repeated Posts

Read the article

News & PublishingAug 27, 2024

Build a news topic taxonomy that survives the next news cycle

Read the article

Explore another practice.

Provenance Guides & Articles

LAB TOPIC / PROVENANCE

ProvenanceKeep the source connected to the statement.

Provenance records where information came from and the context needed to interpret it. These articles show why source identity, timestamps, versions, and attribution belong beside topic labels. Use them to make summaries, archives, and derived records easier to inspect and correct.

6 CONNECTED ARTICLES

Reconstruct the reason behind one record

Choose a topic assignment and work backward until you can identify the exact document revision, the supporting passage, and the definition used. Then repeat the exercise for an extracted statement. Who made it, what conditions accompanied it, and which date describes the statement rather than your retrieval? Use the domain guides to see where these answers belong in distinct fields.

Now remove one piece of context and inspect the effect. A financial amount loses its table caption; a model claim loses its evaluation language; a country reference loses its role in the story. Would the remaining record imply more than the source established? This reading path helps you decide which evidence must travel with an export or summary, and which unresolved details deserve a visible review state instead of a silent assumption.

Applied TopicsAug 18, 2026

Preserve Country and Political Context in Topic Data

Read the article

Applied TopicsJun 9, 2026

Design Prediction Topic Contracts Without Inventing Forecasts

Read the article

News & PublishingNov 25, 2025

Separate Social Topic Signals from Repeated Posts

Read the article

AI & LLMsSep 4, 2025

LLM topic extraction: a JSON contract you can actually validate

Read the article

Applied TopicsNov 7, 2024

Classify Financial Documents Without Losing Entity and Time Context

Read the article

API FoundationsMar 19, 2024

Topics API design: IDs, labels, and useful JSON contracts

Read the article

Explore another practice.

Multilingual topics Guides & Articles

LAB TOPIC / MULTILINGUAL TOPICS

Multilingual topicsPreserve meaning across languages and places.

A translated label can support navigation while the original wording preserves context. Explore language-aware evaluation, geographic names, and the difference between a normalized concept and the expression found in a document. Test the difficult cases with people who understand the language and the task.

2 CONNECTED ARTICLES

Check whether the same concept survives translation

Select a topic definition and compare how it would be applied to documents in two languages your publication uses. Start with clear matches, then include abbreviations, translated institution names, and wording that has no simple equivalent. Use the country-context and taxonomy guides to ask whether the same identifier still describes the same concept, or whether a scope note needs clarification.

Keep three things visible during the exercise: the original passage, any translation used for review, and the label shown in the interface. They serve different purposes. Can a reviewer tell which wording came from the source? Can an ambiguous institution remain unresolved without losing search usefulness? Compare errors within each language collection before interpreting differences in topic counts. Finish with reviewed aliases and boundary examples that preserve the original evidence.

Applied TopicsAug 18, 2026

Preserve Country and Political Context in Topic Data

Read the article

News & PublishingAug 27, 2024

Build a news topic taxonomy that survives the next news cycle

Read the article

Explore another practice.

Responsible AI Guides & Articles

LAB TOPIC / RESPONSIBLE AI

Responsible AIKeep automated claims within the evidence.

Responsible topic workflows make uncertainty, limitations, and attribution visible. These guides discuss model claims, sensitive context, review policies, and the difference between a content label and an assertion about a person or system. Build decisions that can be explained and corrected.

3 CONNECTED ARTICLES

Check what a reader could infer from the label

Inspect the claim created by your interface, including implications that the underlying record never states. Could an AGI discussion badge look like a verified capability? Could a political subject label look like a profile of the author? Could a forecast topic appear to endorse an outcome? Read the model, geographic, and evaluation guides with these specific interpretation risks in mind.

For each case, decide what evidence, attribution, or separate field would keep the meaning clear. Preserve a source's claim without silently adopting it, and let insufficient evidence produce an understandable unresolved result. Ask whether a reviewer can correct the record and whether downstream summaries retain the important qualification. The aim of this reading path is an inspectable publication rule for each consequential inference, supported by examples that your team can consistently review.

Applied TopicsAug 18, 2026

Preserve Country and Political Context in Topic Data

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AI & LLMsMay 16, 2025

Build an AI Model Taxonomy That Keeps Claims in Context

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AI & LLMsFeb 12, 2025

Evaluate AI topic classification before you trust the labels

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Explore another practice.

About TopicsAPI.com

Make the structure understandable.

A label can look simple while hiding a long list of decisions. What does the concept include? Does a passing mention count? Which version of the vocabulary was used? How should an uncertain assignment be handled?

TopicsAPI.com brings those decisions into focus. We publish practical explanations for developers designing topic records, publishers organizing archives, and researchers interpreting automated classification. Our guides connect the vocabulary to the evidence and the workflow that make it useful.

A connected collection of guides

The site begins with Topics API foundations, then follows the same questions into AI, language models, prompts, model catalogs, news, finance, prediction, geographic context, social content, and politics. Each area has its own context; a single category list is rarely enough to explain it.

The Topics API Lab develops those questions through longer articles, concrete examples, and links to relevant primary documentation. Category and tag archives help you follow a practice such as evaluation, provenance, or structured output across different domains.

How we approach the material

We distinguish a topic from an entity, a source statement from a verified result, and a content label from a capability claim. Examples are labeled so readers can adapt them without mistaking them for production service documentation. When an article refers to an external standard or technical definition, its editorial source link appears in the article body.

Clarity also means preserving uncertainty. The guides emphasize explicit review states, representative evaluation, and attribution when the evidence does not justify a confident conclusion. A system should help people inspect and correct its decisions.

Bring a better question.

Have a topic boundary that is difficult to define, a field that keeps changing meaning, or an archive that needs a clearer structure? Contact TopicsAPI.com with the context. Suggestions and corrections help shape a more useful collection.

Contact TopicsAPI.com

CONTACT TOPICSAPI.COM

Let’s talk topics.

Questions, corrections, and ideas for the Lab are welcome. Send an email with the page or topic you have in mind.

info@topicsapi.com

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A USEFUL PLACE TO START

Give us a little context.

Ask a topic question

Describe the content you are organizing, the decision you want to support, and the part of the vocabulary or data contract that needs clarification.

Suggest a correction

Include the page address, the relevant passage, and the source or example that explains the issue. Specific context makes a correction easier to assess.

Propose a Lab article

Tell us which practical problem deserves a deeper guide, who faces it, and what a useful explanation would help the reader do.

Looking for a starting point?

Explore the core vocabulary or browse the full collection of practical articles.

Topics API foundations Topics API Lab