LAB TOPIC / TAXONOMY
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
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.
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 : Prompt design for topic labels: definitions, evidence, and edge cases
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 : Build an AI Model Taxonomy That Keeps Claims in Context
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 : Evaluate AI topic classification before you trust the labels
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 : Classify Financial Documents Without Losing Entity and Time Context
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 : Build a news topic taxonomy that survives the next news cycle
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 : Topics API design: IDs, labels, and useful JSON contracts