guidance

SKILL.md

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

```python
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

# Configure Claude
lm = models.Anthropic("claude-sonnet-4-5-20250929")

# Use context managers for chat format
with system():
    lm += "You are a helpful assistant."

with user():
    lm += "What is the capital of France?"

with assistant():
    lm += gen(max_tokens=20)

Core Concepts

1. Context Managers

Guidance uses Pythonic context managers for chat-style interactions.

from guidance import system, user, assistant, gen

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# System message
with system():
    lm += "You are a JSON generation expert."

# User message
with user():
    lm += "Generate a person object with name and age."

# Assistant response
with assistant():
    lm += gen("response", max_tokens=100)

print(lm["response"])

Benefits:

Natural chat flow

Clear role separation

Easy to read and maintain

2. Constrained Generation

Guidance ensures outputs match specified patterns using regex or grammars.

Regex Constraints

from guidance import models, gen

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# Constrain to valid email format
lm += "Email: " + gen("email", regex=r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")

# Constrain to date format (YYYY-MM-DD)
lm += "Date: " + gen("date", regex=r"\d{4}-\d{2}-\d{2}")

# Constrain to phone number
lm += "Phone: " + gen("phone", regex=r"\d{3}-\d{3}-\d{4}")

print(lm["email"])  # Guaranteed valid email
print(lm["date"])   # Guaranteed YYYY-MM-DD format

How it works:

Regex converted to grammar at token level

Invalid tokens filtered during generation

Model can only produce matching outputs

Selection Constraints

from guidance import models, gen, select

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# Constrain to specific choices
lm += "Sentiment: " + select(["positive", "negative", "neutral"], name="sentiment")

# Multiple-choice selection
lm += "Best answer: " + select(
    ["A) Paris", "B) London", "C) Berlin", "D) Madrid"],
    name="answer"
)

print(lm["sentiment"])  # One of: positive, negative, neutral
print(lm["answer"])     # One of: A, B, C, or D

3. Token Healing

Guidance automatically "heals" token boundaries between prompt and generation.

Problem: Tokenization creates unnatural boundaries.

# Without token healing
prompt = "The capital of France is "
# Last token: " is "
# First generated token might be " Par" (with leading space)
# Result: "The capital of France is  Paris" (double space!)

Solution: Guidance backs up one token and regenerates.

from guidance import models, gen

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# Token healing enabled by default
lm += "The capital of France is " + gen("capital", max_tokens=5)
# Result: "The capital of France is Paris" (correct spacing)

Benefits:

Natural text boundaries

No awkward spacing issues

Better model performance (sees natural token sequences)

4. Grammar-Based Generation

Define complex structures using context-free grammars.

from guidance import models, gen

lm = models.Anthropic("claude-sonnet-4-5-20250929")

# JSON grammar (simplified)
json_grammar = """
{
    "name": <gen name regex="[A-Za-z ]+" max_tokens=20>,
    "age": <gen age regex="[0-9]+" max_tokens=3>,
    "email": <gen email regex="[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}" max_tokens=50>
}
"""

# Generate valid JSON
lm += gen("person", grammar=json_grammar)

print(lm["person"])  # Guaranteed valid JSON structure

Use cases:

Complex structured outputs

Nested data structures

Programming language syntax

Domain-specific languages

5. Guidance Functions

Create reusable generation patterns with the @guidance decorator.

from guidance import guidance, gen, models

@guidance
def generate_person(lm):
    """Generate a person with name and age."""
    lm += "Name: " + gen("name", max_tokens=20, stop="\n")
    lm += "\nAge: " + gen("age", regex=r"[0-9]+", max_tokens=3)
    return lm

# Use the function
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = generate_person(lm)

print(lm["name"])
print(lm["age"])

Stateful Functions:

@guidance(stateless=False)
def react_agent(lm, question, tools, max_rounds=5):
    """ReAct agent with tool use."""
    lm += f"Question: {question}\n\n"

    for i in range(max_rounds):
        # Thought
        lm += f"Thought {i+1}: " + gen("thought", stop="\n")

        # Action
        lm += "\nAction: " + select(list(tools.keys()), name="action")

        # Execute tool
        tool_result = tools[lm["action"]]()
        lm += f"\nObservation: {tool_result}\n\n"

        # Check if done
        lm += "Done? " + select(["Yes", "No"], name="done")
        if lm["done"] == "Yes":
            break

    # Final answer
    lm += "\nFinal Answer: " + gen("answer", max_tokens=100)
    return lm

Backend Configuration

Anthropic Claude

from guidance import models

lm = models.Anthropic(
    model="claude-sonnet-4-5-20250929",
    api_key="your-api-key"  # Or set ANTHROPIC_API_KEY env var
)

OpenAI

lm = models.OpenAI(
    model="gpt-4o-mini",
    api_key="your-api-key"  # Or set OPENAI_API_KEY env var
)

Local Models (Transformers)

from guidance.models import Transformers

lm = Transformers(
    "microsoft/Phi-4-mini-instruct",
    device="cuda"  # Or "cpu"
)

Local Models (llama.cpp)

from guidance.models import LlamaCpp

lm = LlamaCpp(
    model_path="/path/to/model.gguf",
    n_ctx=4096,
    n_gpu_layers=35
)

Common Patterns

Pattern 1: JSON Generation

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

lm = models.Anthropic("claude-sonnet-4-5-20250929")

with system():
    lm += "You generate valid JSON."

with user():
    lm += "Generate a user profile with name, age, and email."

with assistant():
    lm += """{
    "name": """ + gen("name", regex=r'"[A-Za-z ]+"', max_tokens=30) + """,
    "age": """ + gen("age", regex=r"[0-9]+", max_tokens=3) + """,
    "email": """ + gen("email", regex=r'"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"', max_tokens=50) + """
}"""

print(lm)  # Valid JSON guaranteed

Pattern 2: Classification

from guidance import models, gen, select

lm = models.Anthropic("claude-sonnet-4-5-20250929")

text = "This product is amazing! I love it."

lm += f"Text: {text}\n"
lm += "Sentiment: " + select(["positive", "negative", "neutral"], name="sentiment")
lm += "\nConfidence: " + gen("confidence", regex=r"[0-9]+", max_tokens=3) + "%"

print(f"Sentiment: {lm['sentiment']}")
print(f"Confidence: {lm['confidence']}%")

Pattern 3: Multi-Step Reasoning

from guidance import models, gen, guidance

@guidance
def chain_of_thought(lm, question):
    """Generate answer with step-by-step reasoning."""
    lm += f"Question: {question}\n\n"

    # Generate multiple reasoning steps
    for i in range(3):
        lm += f"Step {i+1}: " + gen(f"step_{i+1}", stop="\n", max_tokens=100) + "\n"

    # Final answer
    lm += "\nTherefore, the answer is: " + gen("answer", max_tokens=50)

    return lm

lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = chain_of_thought(lm, "What is 15% of 200?")

print(lm["answer"])

Pattern 4: ReAct Agent

from guidance import models, gen, select, guidance

@guidance(stateless=False)
def react_agent(lm, question):
    """ReAct agent with tool use."""
    tools = {
        "calculator": lambda expr: eval(expr),
        "search": lambda query: f"Search results for: {query}",
    }

    lm += f"Question: {question}\n\n"

    for round in range(5):
        # Thought
        lm += f"Thought: " + gen("thought", stop="\n") + "\n"

        # Action selection
        lm += "Action: " + select(["calculator", "search", "answer"], name="action")

        if lm["action"] == "answer":
            lm += "\nFinal Answer: " + gen("answer", max_tokens=100)
            break

        # Action input
        lm += "\nAction Input: " + gen("action_input", stop="\n") + "\n"

        # Execute tool
        if lm["action"] in tools:
            result = tools[lm["action"]](lm["action_input"])
            lm += f"Observation: {result}\n\n"

    return lm

lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = react_agent(lm, "What is 25 * 4 + 10?")
print(lm["answer"])

Pattern 5: Data Extraction

from guidance import models, gen, guidance

@guidance
def extract_entities(lm, text):
    """Extract structured entities from text."""
    lm += f"Text: {text}\n\n"

    # Extract person
    lm += "Person: " + gen("person", stop="\n", max_tokens=30) + "\n"

    # Extract organization
    lm += "Organization: " + gen("organization", stop="\n", max_tokens=30) + "\n"

    # Extract date
    lm += "Date: " + gen("date", regex=r"\d{4}-\d{2}-\d{2}", max_tokens=10) + "\n"

    # Extract location
    lm += "Location: " + gen("location", stop="\n", max_tokens=30) + "\n"

    return lm

text = "Tim Cook announced at Apple Park on 2024-09-15 in Cupertino."

lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = extract_entities(lm, text)

print(f"Person: {lm['person']}")
print(f"Organization: {lm['organization']}")
print(f"Date: {lm['date']}")
print(f"Location: {lm['location']}")

Best Practices

1. Use Regex for Format Validation

# ✅ Good: Regex ensures valid format
lm += "Email: " + gen("email", regex=r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")

# ❌ Bad: Free generation may produce invalid emails
lm += "Email: " + gen("email", max_tokens=50)

2. Use select() for Fixed Categories

# ✅ Good: Guaranteed valid category
lm += "Status: " + select(["pending", "approved", "rejected"], name="status")

# ❌ Bad: May generate typos or invalid values
lm += "Status: " + gen("status", max_tokens=20)

3. Leverage Token Healing

# Token healing is enabled by default
# No special action needed - just concatenate naturally
lm += "The capital is " + gen("capital")  # Automatic healing

4. Use stop Sequences

# ✅ Good: Stop at newline for single-line outputs
lm += "Name: " + gen("name", stop="\n")

# ❌ Bad: May generate multiple lines
lm += "Name: " + gen("name", max_tokens=50)

5. Create Reusable Functions

# ✅ Good: Reusable pattern
@guidance
def generate_person(lm):
    lm += "Name: " + gen("name", stop="\n")
    lm += "\nAge: " + gen("age", regex=r"[0-9]+")
    return lm

# Use multiple times
lm = generate_person(lm)
lm += "\n\n"
lm = generate_person(lm)

6. Balance Constraints

# ✅ Good: Reasonable constraints
lm += gen("name", regex=r"[A-Za-z ]+", max_tokens=30)

# ❌ Too strict: May fail or be very slow
lm += gen("name", regex=r"^(John|Jane)$", max_tokens=10)

Comparison to Alternatives

Feature
Guidance
Instructor
Outlines
LMQL

Regex Constraints
✅ Yes
❌ No
✅ Yes
✅ Yes

Grammar Support
✅ CFG
❌ No
✅ CFG
✅ CFG

Pydantic Validation
❌ No
✅ Yes
✅ Yes
❌ No

Token Healing
✅ Yes
❌ No
✅ Yes
❌ No

Local Models
✅ Yes
⚠️ Limited
✅ Yes
✅ Yes

API Models
✅ Yes
✅ Yes
⚠️ Limited
✅ Yes

Pythonic Syntax
✅ Yes
✅ Yes
✅ Yes
❌ SQL-like

Learning Curve
Low
Low
Medium
High

When to choose Guidance:

Need regex/grammar constraints

Want token healing

Building complex workflows with control flow

Using local models (Transformers, llama.cpp)

Prefer Pythonic syntax

When to choose alternatives:

Instructor: Need Pydantic validation with automatic retrying

Outlines: Need JSON schema validation

LMQL: Prefer declarative query syntax

Performance Characteristics

Latency Reduction:

30-50% faster than traditional prompting for constrained outputs

Token healing reduces unnecessary regeneration

Grammar constraints prevent invalid token generation

Memory Usage:

Minimal overhead vs unconstrained generation

Grammar compilation cached after first use

Efficient token filtering at inference time

Token Efficiency:

Prevents wasted tokens on invalid outputs

No need for retry loops

Direct path to valid outputs

Resources

Documentation: https://guidance.readthedocs.io

GitHub: https://github.com/guidance-ai/guidance (18k+ stars)

Notebooks: https://github.com/guidance-ai/guidance/tree/main/notebooks

Discord: Community support available

See Also

references/constraints.md - Comprehensive regex and grammar patterns

references/backends.md - Backend-specific configuration

references/examples.md - Production-ready examples