Background
Manually verifying LLM output is not only time consuming, but also prone to missing edge cases.Instructor in conjunction with Pydantic provides an automated and comprehensive verification solution.
Core Solutions
- Automatic type validation: Pydantic automatically checks that the field types match the definitions
- Custom Validators: Add business rules in the model using the @validator decorator
- strict model: set Config.strict=True to reject redundant fields
- Error Handling Integration: automatically collects all validation errors and not just the first one
Advanced Optimization
- Automatically convert validation errors to HTTP responses when using frameworks such as FastAPI.
- Documentation of validation failures through the hook system is used to improve the prompt
- Implementing static type checking in conjunction with mypy
Summary points
The automated validation capability provided by Instructor not only reduces the amount of work involved in handwriting validation code, but also catches more potential problems and improves data quality.
This answer comes from the articleInstructor: a Python library to simplify structured output workflows for large language modelsThe
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