guidance

You can constrain LLM outputs to valid JSON by applying regex and grammar-based constraints during generation. This enforces strict structured data formats and includes token healing to prevent invalid syntax across multi-step workflows.

Grammar-based constrained generation restricts LLM token sampling to specific structures like JSON, XML, or code. It uses Pythonic guidance functions and regex constraints to ensure outputs strictly adhere to required formats and validation rules.

Yes, constrained generation works with local model backends like Transformers and llama.cpp. This enables privacy-preserving, offline development and testing while applying grammar-based generation and strict output validation.

You can enforce regex constraints across multi-step workflows using Pythonic guidance functions. This orchestrates reliable prompts and responses while maintaining strict format validation throughout the generation sequence.

The best way to validate structured outputs without external parsers is using grammar-based generation with token healing. This natively restricts LLM outputs to JSON, XML, or code formats during inference, eliminating the need for post-generation validation.

Yes, constrained generation supports ReAct-style tooling on both cloud and local models. It integrates regex and grammar constraints with Pythonic guidance functions to orchestrate reliable multi-step tool usage and structured responses.