guidance

You constrain LLM outputs for valid JSON by applying context-free grammars during generation. This enforces schema rules directly in the decoding process, ensuring predictable, structure-compliant outputs and eliminating post-generation validation.

Constrained generation controls LLM outputs using regex and grammars to guarantee correct formatting. You need it for data extraction when outputs must strictly match schemas, valid JSON, or XML formats without requiring manual post-editing or validation.

Yes, regex constraints validate structured outputs like emails, dates, and IDs during generation. This guarantees correct formatting by restricting the LLM to only produce tokens matching the specified regex pattern.

Yes, grammar-based generation requires Python and the Guidance library. This setup enables context-managed generation, token healing, and grammar compilation caching to orchestrate end-to-end prompt pipelines.

Build multi-step workflows for structured output using reusable guidance functions and patterns. This orchestrates end-to-end prompt pipelines, combining regex constraints and grammars for production-grade data extraction and schema-constrained content generation.

Token healing matters because it preserves natural token boundaries and minimizes rewrites across generations. This prevents boundary artifacts when applying grammar constraints, maintaining structured data integrity during automated extraction.