Principle:PrefectHQ Prefect Structured AI Output
| Metadata | |
|---|---|
| Source | Doc: pydantic-ai Output |
| Domains | AI_Agents, Data_Validation |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
A pattern for constraining LLM responses to validated, typed Pydantic models ensuring consistent, parseable AI outputs.
Description
Structured AI Output forces LLM responses into predefined Pydantic schemas instead of free-form text. This ensures:
- Type safety — all fields have correct types
- Validation — constraints like min_length, max_length, ge, le are enforced
- Consistency — every response has the same structure
- Parseability — downstream code can reliably access fields
The pattern works by defining a Pydantic BaseModel with typed fields and passing it as output_type to the AI agent.
Usage
Use this pattern whenever an AI agent's output needs to be consumed programmatically by downstream code. Free-form text is fine for display; structured output is essential for automation.
Theoretical Basis
Schema-constrained generation. The LLM's response is parsed and validated against the Pydantic schema. If validation fails, the framework can retry or request corrections. This moves AI output from unstructured text to typed data, similar to how GraphQL schemas constrain API responses.