guidance

You can constrain LLM outputs by applying regex and grammar constraints to enforce valid JSON formats. This ensures structured, compliant results and reduces hallucinations in automated data pipelines.

Grammar-based generation restricts LLM token selection to satisfy predefined regex and grammar rules. This mechanism guarantees structured output, preventing invalid formats during multi-step workflows and data extraction.

Yes, you can apply regex constraints within Pythonic control flows to enforce structured output. This allows you to build multi-step workflows that generate reliable, formatted text for automation pipelines.

Token healing repairs partial tokens at prompt boundaries to enforce strict formatting rules. It speeds up generation pipelines by ensuring reproducible, constrained outputs without requiring multiple sampling attempts.

The best way to extract structured data is applying grammar-based constraints and regex patterns directly during generation. This guarantees valid JSON or XML payloads for reliable form processing and data extraction.

Use constrained generation when you need guaranteed valid JSON, XML, or code formats from LLMs. It is essential for automated data pipelines, form processing, and documentation generation where invalid outputs break workflows.