guidance

You can force valid JSON generation by applying grammar-based structured constraints to the LLM, which enforces syntactic rules at the token level to guarantee the output remains completely parseable and consistently formatted.

The best way to constrain LLM text generation is using regex-based constrained generation, which enforces exact pattern matching for specific fields like emails, dates, and IDs directly during the generation process.

Yes, you can build multi-step workflow agents using token healing and constrained workflows, which allows you to write stateful generation logic in Pythonic code while ensuring outputs remain parseable across every step.

Constrained generation works for data extraction by enforcing regex patterns that require exact matches, ensuring extracted fields like dates in YYYY-MM-DD format or phone numbers strictly adhere to your specified formats.

LLM outputs contain formatting glitches in XML generation because standard models lack syntactic constraints; applying grammar-based structured generation enforces token-level constraints to guarantee format validity and reduce these errors.

Yes, applying grammar constraints for structured output requires the transformers library along with the guidance dependency to map CFG-style grammars and regex validation directly onto the language model generation process.