guidance

Constrained generation enforces valid JSON output from an LLM by applying regex patterns and grammar rules during decoding, preventing malformed fields and syntax errors without repeated validation.

Constrained generation for AI agents restricts language model outputs to predefined formats and syntax, ensuring multi-step workflows and extraction pipelines reliably receive valid structured data.

Build a multi-step LLM data extraction workflow by using Pythonic orchestration functions to chain models, applying grammar constraints to extract validated records from unstructured text inputs.

Yes, you can apply regex constraints to validate and control LLM responses across both API and local model backends, ensuring generated text strictly matches required patterns.

This approach works with both API and local model backends, allowing you to enforce structured outputs and apply grammar constraints regardless of your deployment environment.

LLMs output malformed structured data because prompt instructions alone cannot guarantee syntax compliance; applying grammar-based generation and token healing directly constrains the decoding process.