Structured Outputs: Getting Reliable JSON From LLMs

Structured outputs make LLM responses machine-readable: instead of prose, the model returns JSON that matches a schema you define. This is the foundation for building reliable software on top of models.

Why structured outputs matter

• No parsing guesswork — the response is valid JSON.
• Type-safe integration with your code.
• Consistent fields for agents and pipelines.
• Fewer silent failure modes than free-text replies.

JSON mode vs schema enforcement

JSON mode guarantees valid JSON but not a specific shape. Schema-based structured outputs guarantee both valid JSON and fields matching your schema — worth using when your code depends on exact fields.

Prompting patterns

1. Describe the output as JSON with an example.
2. List required fields, types, and constraints.
3. Ask the model to fill values from the input only.
4. Add a fallback instruction for missing data (use null).

Validate on your side

Even with structured outputs, validate the response against your schema at runtime and handle failures. Models occasionally emit nulls, wrong enums, or refusals — a validation layer turns those into caught errors.

Cost consideration

FAQ