Request structured, usable outputs
Specify schemas and constraints, then validate generated results in code.
- Design an output contract and explain why generated data needs validation.
When another program will consume a response, define a simple output contract: required fields, allowed values, and what to do when a value is unknown. A model can be asked to return JSON or another format, but a prompt alone does not guarantee valid structure. Parse and validate the output before using it, and handle missing fields or invalid values safely.
A small example
1record = {"title": "Meeting", "date": "2026-10-02"}
2required = {"title", "date"}
3valid = required.issubset(record)
4print("valid record" if valid else "missing fields")valid record
For supported model APIs, schema-constrained output features can enforce more structure than plain text instructions. Even then, check semantic requirements such as whether a date is plausible or supported by the source. Keep user-facing presentation separate from machine-readable fields where possible.
Key takeaways
Design an output contract and explain why generated data needs validation.
Test prompts on realistic inputs and validate important outputs in software.
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