Code that consumes model output needs it in a fixed shape, usually JSON that matches a schema. Asking for JSON works most of the time, which is not enough in production. Structured output features in model APIs, constrained decoding and validation with libraries such as Pydantic close the gap: the schema defines the fields and types, the model is held to it, and anything that still fails validation is retried or rejected.
A fixed schema also helps safety and quality, since it leaves less room for injected instructions and makes confidence fields and checks easy to add. These guides cover getting reliable JSON, extracting data from images, validating outputs, planning tool calls as JSON and hardening a model's output when prompting is not enough.
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