Structured Output
Structured output makes a model follow a machine-readable schema instead of writing any form of prose.
Another example
An extraction task returns checked JSON with fields for title, date, currency, and confidence.
Main parts
- 01User intent and context
- 02Model or tool decision
- 03Grounded result and fallback
Use it when
Use it when software must read a result or send it to the right place.
Do not use it when
A valid schema doesn't make the facts correct. Check the output with domain rules too.
Name used in code
JSON Schema Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Structured Output to make AI behavior measurable and tied to evidence. User context: An extraction task returns checked JSON with fields for title, date, currency, and confidence. AI method or concept: Structured Output. Why it fits: Use it when software must read a result or send it to the right place. Do not use it when: A valid schema doesn't make the facts correct. Check the output with domain rules too. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Structured Output. Definition: Structured output makes a model follow a machine-readable schema instead of writing any form of prose. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.