What is structured output? Schema conformity does not establish factual truth
Explain field constraints and distinguish valid JSON, schema conformity and factual correctness, including unknowns and failures.
Understand the concept
Structured output constrains model fields and types within interface support. It helps parsing without guaranteeing source-grounded values. Valid JSON differs from schema conformity and missing dates need unknowns. Check request state, structure and facts separately; parsing is not verification.
Type constraints do not prove field evidence
OpenAI and Anthropic support structured outputs with different schema subsets. Correct string or numeric types can still hold wrong facts. Formatting, evidence and cross-field business consistency differ. Define missing values rather than guesses and handle refusals and interruptions before parsing defaults that conceal failure.
Check structure and missing facts against one source
Extract names and dates from a public form, check types and originals and test a missing-date version. Ensure defined unknowns rather than plausible invented dates. Test failures against default-record storage. No model or parser was run here. With streams, wait for completion; neither reject interim partial objects nor invent absent fields.
Synchronize schema updates with downstream consumers
Version schemas and replay consumers after field or enum changes. Recheck model support rather than transferring schema assumptions across providers. Include compatibility and fact review. Combine deterministic fields with generated explanations where useful. Report structural and factual errors separately instead of advertising error-free answers.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.