instructor

To extract structured data from LLM responses with Pydantic validation, this Skill automates schema enforcement and type-safe parsing. It leverages the Instructor library to intercept outputs, validate them against your Pydantic models, and automatically retry failed extractions.

Yes, structured data extraction works with both OpenAI and Anthropic providers. The Skill requires their respective provider clients and supports multi-provider compatibility, allowing you to enforce Pydantic validation and stream partial results across different LLM environments.

The best way to handle LLM validation failures during data extraction is using automatic retries with helpful error feedback. When Pydantic validation fails, the Skill feeds the error back to the LLM, prompting it to correct the output and successfully conform to the schema.

Yes, you can stream partial LLM outputs for real-time processing. The Skill supports streaming partial results across providers, enabling type-safe JSON parsing of complex and nested structures as they generate, rather than waiting for the full response.

You need the Instructor library because it orchestrates the core extraction logic, connecting Pydantic validation with LLM provider clients. It manages the automatic retry loops and streaming capabilities required to reliably enforce schema correctness on unstructured text.

A limitation of streaming partial structured data from LLMs is that nested structures may be incomplete until the full response is received. While the Skill provides real-time parsing, complex validation rules might still require waiting for the final output to fully pass Pydantic checks.