guidance

To constrain LLM outputs to generate valid JSON and XML, you apply regex and context-free grammars during the generation process, which guarantees structured outputs and eliminates invalid formatting. This approach reduces the need for post-processing.

Constrained generation is a workflow that uses regex and grammars to enforce valid structured outputs from large language models. It ensures robust validation and applies token healing to automatically correct partial tokens at generation boundaries.

Yes, you can use grammars and regex for form validation and data extraction by defining a constrained generation workflow. This guarantees that the extracted data strictly matches your required JSON or XML schemas without requiring extensive manual post-processing.

When grammar constraints conflict or inputs are incomplete, the constrained generation workflow applies graceful fallbacks to handle the errors. This ensures the process continues safely without crashing, maintaining robust validation throughout the generation task.

Yes, constrained generation works with safe, local backends to securely automate report generation and validate structured outputs. You can define multi-step workflows that apply regex and grammars locally to ensure data privacy and robust validation.

The best way to guarantee structured outputs for automated content generation is to implement a constrained generation workflow using regex and CFG grammars. This method enforces valid JSON or XML formats, utilizing token healing and graceful fallbacks for robust results.