LAB TOPIC / STRUCTURED OUTPUT
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
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.
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 : Design Prediction Topic Contracts Without Inventing Forecasts
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
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 : LLM topic extraction: a JSON contract you can actually validate