Skip to content
FindOpenSource

Guidance

Programming paradigm and library for controlling LLM generation with interleaved prompting, control flow, and constrained output in one program.

Overview

Guidance lets a developer write a single program that mixes plain text prompts, control flow (loops, conditionals), and constraints on exactly what the model can generate next - regex patterns, choice lists, or structured formats - rather than making multiple separate calls to an LLM API and stitching results together in application code. This can also make generation faster by skipping tokens that are already determined by the constraints.

Guidance is a fit for teams that want fine-grained, programmatic control over the generation process itself, a compared alternative to Outlines (also in this catalog) - both constrain LLM output, with Guidance emphasizing an interleaved program-and-generation authoring style.

Categories
AI & Machine Learning
Keywords
structured-outputllm-generationprompt-engineering
Languages
Python
License
MIT

Spotted an error? Suggest an edit on GitHub.