guidance

To constrain LLM outputs and guarantee valid JSON, you apply regex and grammar constraints during generation, forcing the model to produce syntactically correct structured data while reducing invalid results.

Grammar-based generation controls language models by defining strict structural rules that the output must follow, ensuring that generated text conforms to specified formats like JSON or XML rather than relying on post-hoc validation.

Yes, you can use regex to enforce structured formats for LLM data extraction, applying pattern constraints directly during generation to ensure outputs match exact specifications for reliable data interchange.

The best way to build multi-step workflows for constrained generation is using Pythonic orchestration to chain generation steps, applying token healing to manage complex tasks end-to-end across different backends.

Constrained generation works across different LLM backends by applying Pythonic orchestration and grammar-based rules, allowing you to maintain consistent structured outputs regardless of the underlying model provider.

You need token healing when validating structured LLM outputs because it repairs token boundaries during generation, preventing partial tokens from corrupting regex and grammar constraints in multi-step workflows.