What structured output is
Structured output is the practice of forcing a language model to return its answer in a fixed, machine-readable format — almost always JSON that conforms to a schema you define — instead of free-form prose. Rather than parsing a paragraph and hoping to extract the right values, your code receives clean, predictable data it can use directly. This is essential whenever an LLM feeds into software: data extraction, classification, form filling, and powering function calls all depend on it.
How it works
A normal LLM picks each next token freely, so it might wrap JSON in markdown, add an explanation, or produce a stray comma that breaks parsing. Structured output solves this with constrained decoding: during generation the model is only allowed to choose tokens that keep the output valid against the target grammar or schema. Common approaches include:
- JSON mode — the provider guarantees the response is syntactically valid JSON.
- Schema-constrained / grammar-constrained decoding — the output must match a specific JSON Schema (or a grammar such as GBNF), so even field names and types are enforced.
Because invalid tokens are masked out before sampling, the result is valid by construction rather than by luck.
Build a structured-output prompt
Use the tool below to define the fields you want and generate a ready-to-use prompt — a system instruction, a JSON schema, and an example output — that you can paste into any LLM that supports JSON mode.
Tips for reliable structured output
- Always validate the values. Constrained decoding guarantees the shape, not the correctness — verify the data against your schema and business rules.
- Describe every field. A one-line description per field dramatically improves extraction accuracy.
- Allow nulls or “unknown”. Give the model a valid way to say it could not find a value, so it does not invent one.
- Keep schemas focused. Smaller, purpose-built schemas are more reliable than one sprawling object that tries to capture everything at once.