What problem does it solve?
Guidance removes the uncertainty of free-form model prompting by constraining LLM output so it matches the format, schema, or workflow you need. It helps prevent invalid JSON, malformed code, inconsistent classifications, and brittle retry loops.
Core Features & Use Cases
- Regex and grammar constraints for exact formats such as IDs, emails, dates, JSON, XML, CSV, and code.
- Workflow orchestration for multi-step reasoning, ReAct-style tool use, extraction pipelines, and agent coordination.
- Backend flexibility across Anthropic, OpenAI, Transformers, and llama.cpp with token healing and selection controls.
- Use Case: Build a production parser that extracts entities from text and returns validated structured output without manual post-processing.
Quick Start
Ask Guidance to generate a valid structured response, such as a JSON object or classified label, using constrained generation and the backend of your choice.