Structured Outputs
Structured outputs are model responses generated under a machine-readable schema or grammar so that downstream software can parse their shape reliably. In current LLM APIs, a developer commonly supplies a supported JSON Schema and the inference system restricts generation to compatible tokens. This controls syntax and field structure; it does not establish that the values inside those fields are factually correct.
Origin and context
Grammar-constrained decoding predates the branded API feature. A 2023 EMNLP paper showed how input-dependent grammars could support varied structured NLP tasks without task-specific fine-tuning. OpenAI launched Structured Outputs in August 2024, contrasting schema adherence with JSON mode, which only targets valid JSON. Google subsequently documented structured outputs for Gemini, making the pattern cross-provider even though supported schema subsets and failure behavior differ.
Why it matters
Applications often need a typed object, tool argument, classification label, or extracted record rather than prose. Constraining the output reduces parser failures, retry loops, and brittle string repair, and it makes interface contracts easier to test. The gain is structural reliability, not semantic reliability: a perfectly valid object can still contain an invented identifier, a wrong amount, or a value that violates a business rule.
Example
An invoice workflow can request an object containing supplier, invoice number, currency, line items, totals, and an explicit uncertainty field. The application validates the returned object against its own domain rules before writing anything. It separately handles refusals, truncation, and unsupported schemas, and keeps a human review step for consequential discrepancies instead of treating successful parsing as proof of extraction accuracy.
How it differs
Tool Use and Function Calling
Tool or function calling lets a model select an operation and propose its arguments. Structured output is the broader mechanism that constrains a response to a schema; it can format tool arguments, but it can also return typed data without invoking any tool. A valid call still needs authorization and business validation.
Prompt engineering
A prompt can ask for JSON, but wording alone does not restrict the decoder to schema-valid tokens. Structured-output systems combine instructions with schema-aware enforcement. Prompt design still matters for the meaning of fields and the quality of their values.
Maturity and evidence
Maturity is rated 4. The underlying decoding method has peer-reviewed evidence, and multiple major providers expose documented schema-constrained interfaces. It remains below 5 because vendors support different schema subsets, models can refuse or stop early, and no format constraint guarantees correct content.
Limits and open questions
Schemas may require preprocessing and add first-request latency, and complex or recursive structures are not uniformly supported. Refusals and incomplete generations need explicit branches. Schema evolution can also break consumers even when each individual response is valid. Teams should version contracts, test representative edge cases, validate semantics after parsing, and avoid presenting a provider-specific guarantee as a universal property of every model or decoding stack.
Related terms
References
- Introducing Structured Outputs in the APIOpenAI · 2024-08-06 · class A
- Structured outputsGoogle AI for Developers · 2026-09-02 · class A
- Grammar-Constrained Decoding for Structured NLP Tasks without FinetuningEMNLP / arXiv · 2023-05-23 · class A
Last updated: 2026-09-03
This term is also covered in the Skills Atlas as structured llm outputs skill.
This term is also covered in the Skills Atlas as openai api skill.