A mode of AI model generation in which the output is constrained to a specific format — such as JSON or XML — ensuring machine-parseable, predictable responses.
Structured output (also called constrained generation or JSON mode) forces an LLM to produce output that conforms to a predefined schema. Rather than generating free-form text that might or might not be parseable, the model is constrained — at the token level — to only produce valid outputs matching the schema.
This is essential for integrating AI into software pipelines. When an AI agent needs to hand off data to another system, a database, or an API, unstructured text is difficult to parse reliably. Structured outputs guarantee that fields like customer_id, sentiment, and confidence_score always appear in the expected format and type.
OpenAI, Anthropic, and Google all support structured output natively in their APIs via JSON schema or tool-use constraints. Libraries like Instructor (Python) and Outlines make it easy to enforce Pydantic schemas on top of any LLM. Structured output is now a standard requirement for production AI systems, as it removes the fragile "parse the AI's text" step from pipelines.
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