Structured Output
定義
Structured output is the practice of having an LLM return data in a predefined schema — typed fields with specific names, types, and validation rules — rather than free-form natural language. This makes AI output machine-readable and suitable for downstream automation: feeding into databases, triggering conditional logic, populating dashboards, or passing to the next workflow step.
Why Structure Matters
Free-form text requires parsing, which is fragile and error-prone. Structured output with schemas ensures every AI response has the exact fields your system expects, in the correct types, validated before downstream use. This is the foundation of reliable AI automation — without structured output, you can't chain AI tasks together reliably.
How JieGou Enforces Structure
Every JieGou recipe defines an output schema. The recipe executor prompts the LLM to respond in the schema's format, then validates the response. If fields are missing or malformed, the system can retry or flag the run for review. This schema enforcement is what makes recipes composable into multi-step workflows.