Guidance

Guidance: Constrained LLM Generation

When to Use This Skill

Use Guidance when you need to:

• Control LLM output syntax with regex or grammars
• Guarantee valid JSON/XML/code generation
• Reduce latency vs traditional prompting approaches
• Enforce structured formats (dates, emails, IDs, etc.)
• Build multi-step workflows with Pythonic control flow
• Prevent invalid outputs through grammatical constraints

GitHub Stars: 18,000+ | From: Microsoft Research

Installation

# Base installation
pip install guidance

# With specific backends
pip install guidance[transformers]  # Hugging Face models
pip install guidance[llama_cpp]     # llama.cpp models

Quick Start

Basic Example: Structured Generation

from guidance import models, gen

# Load model (supports OpenAI, Transformers, llama.cpp)
lm = models.OpenAI("gpt-4")

# Generate with constraints
result = lm + "The capital of France is " + gen("capital", max_tokens=5)

print(result["capital"])  # "Paris"

With Anthropic Claude

from guidance import models, gen, system, user, assistant