Pydantic as an AI Architecture Boundary
LLM output is not application state until it passes a typed boundary. Schemas, validation errors, evidence fields, and versioned contracts make AI workflows easier to inspect and change.
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LLM output is not application state until it passes a typed boundary. Schemas, validation errors, evidence fields, and versioned contracts make AI workflows easier to inspect and change.
Giant prompts feel productive at demo stage. In production, workflow logic needs to move into code, schemas, routing, validation, evidence, review, and evaluation.