guidance

Constrained generation enforces structured JSON outputs by applying regex and grammars directly during LLM decoding. This guarantees valid formats for data extraction and API scaffolding without relying on post-processing validation.

Grammar-based generation controls LLM decoding by enforcing strict syntax rules to produce deterministic outputs. It guarantees valid JSON, XML, or code by constraining the model to only generate tokens that match the defined grammar pattern.

Yes, constrained generation works with local Transformers models and llama.cpp backends. This allows fast, private experimentation and production deployment while maintaining strict output formatting and validation rules.

The best way to validate LLM outputs for API workflows is grammar-based constrained generation. It enforces strict formats like JSON or XML during decoding, ensuring deterministic and safe structured automation without fragile post-generation parsing.

You orchestrate multi-step LLM workflows with strict formatting by using Pythonic workflow orchestration combined with constrained generation. This applies grammars and token healing across steps to guarantee valid outputs for complex document automation tasks.