instructor

You can validate LLM outputs against a Pydantic schema by passing the model to the extraction function, which enforces type-safe data generation and automatically retries with contextual error feedback if validation fails.

The best way to get structured data from LLM responses is using a validation library that maps outputs directly to Pydantic models, ensuring type safety while supporting automatic retries and partial streaming for real-time feedback.

Yes, structured output extraction works with Anthropic and OpenAI, offering multi-provider compatibility to ensure consistent schema validation and type-safe results across different LLM platforms.

You can stream partial structured results from an LLM by enabling the streaming feature, which allows you to observe data extraction progress in real-time as the model generates content into your Pydantic models.

LLM structured data extraction fails validation when the generated output does not match the defined Pydantic schema, triggering an error-driven feedback loop that attempts automatic retries with contextual error messages to guide the next generation.