guidance

Constrained LLM generation guarantees valid JSON by applying regex or context-free grammars during token-level production. This enforces structural syntax and field-level rules, preventing malformed outputs and eliminating the need for brittle prompt engineering.

Token healing reduces spacing and token-boundary artifacts during constrained generation. It ensures generated text concatenates naturally into well-formed results, producing clean syntax for structured outputs like JSON and XML.

You can enforce regex constraints by applying grammar-based decoding to control token production for specific fields. This validates formats like emails, IDs, and dates during generation, ensuring extracted data matches required patterns.

Yes, you can build stateful or stateless multi-step agent workflows using constrained generation. This assembles complex pipelines with optional selection constraints for fixed categorical choices without relying on brittle prompt engineering.

Yes, constrained generation works with the transformers library. The implementation uses guidance alongside transformers to apply grammar-based decoding, regex constraints, and token healing to control LLM output formats.

Grammar-based decoding limits generation flexibility, as strict regex or context-free grammar rules restrict open-ended text. It is best suited for structured formats like JSON, XML, and code rather than creative text generation.