Production-Ready Examples
Real-world examples of using Guidance for structured generation, agents, and workflows.
Table of Contents
- - JSON Generation
- - Data Extraction
- - Classification Systems
- - Agent Systems
- - Multi-Step Workflows
- - Code Generation
- - Production Tips
- - Guidance Notebooks: https://github.com/guidance-ai/guidance/tree/main/notebooks
- - Guidance Docs: https://guidance.readthedocs.io
- - Community Examples: https://github.com/guidance-ai/guidance/discussions
JSON Generation
Basic JSON
`python
from guidance import models, gen, guidance
@guidance
def generate_user(lm):
"""Generate valid user JSON."""
lm += "{\n"
lm += ' "name": ' + gen("name", regex=r'"[A-Za-z ]+"') + ",\n"
lm += ' "age": ' + gen("age", regex=r"[0-9]+") + ",\n"
lm += ' "email": ' + gen(
"email",
regex=r'"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"'
) + "\n"
lm += "}"
return lm
Use it
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm += "Generate a user profile:\n"
lm = generate_user(lm)
print(lm)
Output: Valid JSON guaranteed
`
Nested JSON
`python
@guidance
def generate_order(lm):
"""Generate nested order JSON."""
lm += "{\n"
# Customer info
lm += ' "customer": {\n'
lm += ' "name": ' + gen("customer_name", regex=r'"[A-Za-z ]+"') + ",\n"
lm += ' "email": ' + gen(
"customer_email",
regex=r'"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"'
) + "\n"
lm += " },\n"
# Order details
lm += ' "order": {\n'
lm += ' "id": ' + gen("order_id", regex=r'"ORD-[0-9]{6}"') + ",\n"
lm += ' "date": ' + gen("order_date", regex=r'"\d{4}-\d{2}-\d{2}"') + ",\n"
lm += ' "total": ' + gen("order_total", regex=r"[0-9]+\.[0-9]{2}") + "\n"
lm += " },\n"
# Status
lm += ' "status": ' + gen(
"status",
regex=r'"(pending|processing|shipped|delivered)"'
) + "\n"
lm += "}"
return lm
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = generate_order(lm)
`
JSON Array
`python
@guidance
def generate_user_list(lm, count=3):
"""Generate JSON array of users."""
lm += "[\n"
for i in range(count):
lm += " {\n"
lm += ' "id": ' + gen(f"id_{i}", regex=r"[0-9]+") + ",\n"
lm += ' "name": ' + gen(f"name_{i}", regex=r'"[A-Za-z ]+"') + ",\n"
lm += ' "active": ' + gen(f"active_{i}", regex=r"(true|false)") + "\n"
lm += " }"
if i < count - 1:
lm += ","
lm += "\n"
lm += "]"
return lm
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = generate_user_list(lm, count=5)
`
Dynamic JSON Schema
`python
import json
from guidance import models, gen, guidance
@guidance
def json_from_schema(lm, schema):
"""Generate JSON matching a schema."""
lm += "{\n"
fields = list(schema["properties"].items())
for i, (field_name, field_schema) in enumerate(fields):
lm += f' "{field_name}": '
# Handle different types
if field_schema["type"] == "string":
if "pattern" in field_schema:
lm += gen(field_name, regex=f'"{field_schema["pattern"]}"')
else:
lm += gen(field_name, regex=r'"[^"]+"')
elif field_schema["type"] == "number":
lm += gen(field_name, regex=r"[0-9]+(\.[0-9]+)?")
elif field_schema["type"] == "integer":
lm += gen(field_name, regex=r"[0-9]+")
elif field_schema["type"] == "boolean":
lm += gen(field_name, regex=r"(true|false)")
if i < len(fields) - 1:
lm += ","
lm += "\n"
lm += "}"
return lm
Define schema
schema = {
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer"},
"score": {"type": "number"},
"active": {"type": "boolean"}
}
}
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = json_from_schema(lm, schema)
`
Data Extraction
Extract from Text
`python
from guidance import models, gen, guidance, system, user, assistant
@guidance
def extract_person_info(lm, text):
"""Extract structured info from text."""
lm += f"Text: {text}\n\n"
with assistant():
lm += "Name: " + gen("name", regex=r"[A-Za-z ]+", stop="\n") + "\n"
lm += "Age: " + gen("age", regex=r"[0-9]+", max_tokens=3) + "\n"
lm += "Occupation: " + gen("occupation", regex=r"[A-Za-z ]+", stop="\n") + "\n"
lm += "Email: " + gen(
"email",
regex=r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}",
stop="\n"
) + "\n"
return lm
text = "John Smith is a 35-year-old software engineer. Contact: john@example.com"
lm = models.Anthropic("claude-sonnet-4-5-20250929")
with system():
lm += "You extract structured information from text."
with user():
lm = extract_person_info(lm, text)
print(f"Name: {lm['name']}")
print(f"Age: {lm['age']}")
print(f"Occupation: {lm['occupation']}")
print(f"Email: {lm['email']}")
`
Multi-Entity Extraction
`python
@guidance
def extract_entities(lm, text):
"""Extract multiple entity types."""
lm += f"Analyze: {text}\n\n"
# Person entities
lm += "People:\n"
for i in range(3): # Up to 3 people
lm += f"- " + gen(f"person_{i}", regex=r"[A-Za-z ]+", stop="\n") + "\n"
# Organization entities
lm += "\nOrganizations:\n"
for i in range(2): # Up to 2 orgs
lm += f"- " + gen(f"org_{i}", regex=r"[A-Za-z0-9 ]+", stop="\n") + "\n"
# Dates
lm += "\nDates:\n"
for i in range(2): # Up to 2 dates
lm += f"- " + gen(f"date_{i}", regex=r"\d{4}-\d{2}-\d{2}", stop="\n") + "\n"
# Locations
lm += "\nLocations:\n"
for i in range(2): # Up to 2 locations
lm += f"- " + gen(f"location_{i}", regex=r"[A-Za-z ]+", stop="\n") + "\n"
return lm
text = """
Tim Cook and Satya Nadella met at Microsoft headquarters in Redmond on 2024-09-15
to discuss the collaboration between Apple and Microsoft. The meeting continued
in Cupertino on 2024-09-20.
"""
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = extract_entities(lm, text)
`
Batch Extraction
`python
@guidance
def batch_extract(lm, texts):
"""Extract from multiple texts."""
lm += "Batch Extraction Results:\n\n"
for i, text in enumerate(texts):
lm += f"=== Item {i+1} ===\n"
lm += f"Text: {text}\n"
lm += "Name: " + gen(f"name_{i}", regex=r"[A-Za-z ]+", stop="\n") + "\n"
lm += "Sentiment: " + gen(
f"sentiment_{i}",
regex=r"(positive|negative|neutral)",
stop="\n"
) + "\n\n"
return lm
texts = [
"Alice is happy with the product",
"Bob is disappointed with the service",
"Carol has no strong feelings either way"
]
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = batch_extract(lm, texts)
`
Classification Systems
Sentiment Analysis
`python
from guidance import models, select, gen
lm = models.Anthropic("claude-sonnet-4-5-20250929")
text = "This product is absolutely amazing! Best purchase ever."
lm += f"Text: {text}\n\n"
lm += "Sentiment: " + select(
["positive", "negative", "neutral"],
name="sentiment"
)
lm += "\nConfidence: " + gen("confidence", regex=r"[0-9]{1,3}") + "%\n"
lm += "Reasoning: " + gen("reasoning", stop="\n", max_tokens=50)
print(f"Sentiment: {lm['sentiment']}")
print(f"Confidence: {lm['confidence']}%")
print(f"Reasoning: {lm['reasoning']}")
`
Multi-Label Classification
`python
@guidance
def classify_article(lm, text):
"""Classify article with multiple labels."""
lm += f"Article: {text}\n\n"
# Primary category
lm += "Primary Category: " + select(
["Technology", "Business", "Science", "Politics", "Entertainment"],
name="primary_category"
) + "\n"
# Secondary categories (up to 3)
lm += "\nSecondary Categories:\n"
categories = ["Technology", "Business", "Science", "Politics", "Entertainment"]
for i in range(3):
lm += f"{i+1}. " + select(categories, name=f"secondary_{i}") + "\n"
# Tags
lm += "\nTags: " + gen("tags", stop="\n", max_tokens=50) + "\n"
# Target audience
lm += "Target Audience: " + select(
["General", "Expert", "Beginner"],
name="audience"
)
return lm
article = """
Apple announced new AI features in iOS 18, leveraging machine learning to improve
battery life and performance. The company's stock rose 5% following the announcement.
"""
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = classify_article(lm, article)
`
Intent Classification
`python
@guidance
def classify_intent(lm, message):
"""Classify user intent."""
lm += f"User Message: {message}\n\n"
# Intent
lm += "Intent: " + select(
["question", "complaint", "request", "feedback", "other"],
name="intent"
) + "\n"
# Urgency
lm += "Urgency: " + select(
["low", "medium", "high", "critical"],
name="urgency"
) + "\n"
# Department
lm += "Route To: " + select(
["support", "sales", "billing", "technical"],
name="department"
) + "\n"
# Sentiment
lm += "Sentiment: " + select(
["positive", "neutral", "negative"],
name="sentiment"
)
return lm
message = "My account was charged twice for the same order. Need help ASAP!"
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = classify_intent(lm, message)
print(f"Intent: {lm['intent']}")
print(f"Urgency: {lm['urgency']}")
print(f"Department: {lm['department']}")
`
Agent Systems
ReAct Agent
`python
from guidance import models, gen, select, guidance
@guidance(stateless=False)
def react_agent(lm, question, tools, max_rounds=5):
"""ReAct agent with tool use."""
lm += f"Question: {question}\n\n"
for round in range(max_rounds):
# Thought
lm += f"Thought {round+1}: " + gen("thought", stop="\n", max_tokens=100) + "\n"
# Action selection
lm += "Action: " + select(
list(tools.keys()) + ["answer"],
name="action"
)
if lm["action"] == "answer":
lm += "\n\nFinal Answer: " + gen("answer", max_tokens=200)
break
# Action input
lm += "\nAction Input: " + gen("action_input", stop="\n", max_tokens=100) + "\n"
# Execute tool
if lm["action"] in tools:
try:
result = toolslm["action"]
lm += f"Observation: {result}\n\n"
except Exception as e:
lm += f"Observation: Error - {str(e)}\n\n"
return lm
Define tools
tools = {
"calculator": lambda expr: eval(expr),
"search": lambda query: f"Search results for '{query}': [Mock results]",
"weather": lambda city: f"Weather in {city}: Sunny, 72°F"
}
Use agent
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = react_agent(lm, "What is (25 * 4) + 10?", tools)
print(lm["answer"])
`
Multi-Agent System
`python
@guidance
def coordinator_agent(lm, task):
"""Coordinator that delegates to specialists."""
lm += f"Task: {task}\n\n"
# Determine which specialist to use
lm += "Specialist: " + select(
["researcher", "writer", "coder", "analyst"],
name="specialist"
) + "\n"
lm += "Reasoning: " + gen("reasoning", stop="\n", max_tokens=100) + "\n"
return lm
@guidance
def researcher_agent(lm, query):
"""Research specialist."""
lm += f"Research Query: {query}\n\n"
lm += "Findings:\n"
for i in range(3):
lm += f"{i+1}. " + gen(f"finding_{i}", stop="\n", max_tokens=100) + "\n"
return lm
@guidance
def writer_agent(lm, topic):
"""Writing specialist."""
lm += f"Topic: {topic}\n\n"
lm += "Title: " + gen("title", stop="\n", max_tokens=50) + "\n"
lm += "Content:\n" + gen("content", max_tokens=500)
return lm
Coordination workflow
task = "Write an article about AI safety"
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = coordinator_agent(lm, task)
specialist = lm["specialist"]
if specialist == "researcher":
lm = researcher_agent(lm, task)
elif specialist == "writer":
lm = writer_agent(lm, task)
`
Tool Use with Validation
`python
@guidance(stateless=False)
def validated_tool_agent(lm, question):
"""Agent with validated tool calls."""
tools = {
"add": lambda a, b: float(a) + float(b),
"multiply": lambda a, b: float(a) * float(b),
"divide": lambda a, b: float(a) / float(b) if float(b) != 0 else "Error: Division by zero"
}
lm += f"Question: {question}\n\n"
for i in range(5):
# Select tool
lm += "Tool: " + select(list(tools.keys()) + ["done"], name="tool")
if lm["tool"] == "done":
lm += "\nAnswer: " + gen("answer", max_tokens=100)
break
# Get validated numeric arguments
lm += "\nArg1: " + gen("arg1", regex=r"-?[0-9]+(\.[0-9]+)?") + "\n"
lm += "Arg2: " + gen("arg2", regex=r"-?[0-9]+(\.[0-9]+)?") + "\n"
# Execute
result = toolslm["tool"]
lm += f"Result: {result}\n\n"
return lm
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = validated_tool_agent(lm, "What is (10 + 5) * 3?")
`
Multi-Step Workflows
Chain of Thought
`python
@guidance
def chain_of_thought(lm, question):
"""Multi-step reasoning with CoT."""
lm += f"Question: {question}\n\n"
# Generate reasoning steps
lm += "Let me think step by step:\n\n"
for i in range(4):
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", stop="\n", max_tokens=50)
return lm
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = chain_of_thought(lm, "If a train travels 60 mph for 2.5 hours, how far does it go?")
print(lm["answer"])
`
Self-Consistency
`python
@guidance
def self_consistency(lm, question, num_samples=3):
"""Generate multiple reasoning paths and aggregate."""
lm += f"Question: {question}\n\n"
answers = []
for i in range(num_samples):
lm += f"=== Attempt {i+1} ===\n"
lm += "Reasoning: " + gen(f"reasoning_{i}", stop="\n", max_tokens=100) + "\n"
lm += "Answer: " + gen(f"answer_{i}", stop="\n", max_tokens=50) + "\n\n"
answers.append(lm[f"answer_{i}"])
# Aggregate (simple majority vote)
from collections import Counter
most_common = Counter(answers).most_common(1)[0][0]
lm += f"Final Answer (by majority): {most_common}\n"
return lm
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = self_consistency(lm, "What is 15% of 200?")
`
Planning and Execution
`python
@guidance
def plan_and_execute(lm, goal):
"""Plan tasks then execute them."""
lm += f"Goal: {goal}\n\n"
# Planning phase
lm += "Plan:\n"
num_steps = 4
for i in range(num_steps):
lm += f"{i+1}. " + gen(f"plan_step_{i}", stop="\n", max_tokens=100) + "\n"
# Execution phase
lm += "\nExecution:\n\n"
for i in range(num_steps):
lm += f"Step {i+1}: {lm[f'plan_step_{i}']}\n"
lm += "Status: " + select(["completed", "in-progress", "blocked"], name=f"status_{i}") + "\n"
lm += "Result: " + gen(f"result_{i}", stop="\n", max_tokens=150) + "\n\n"
# Summary
lm += "Summary: " + gen("summary", max_tokens=200)
return lm
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = plan_and_execute(lm, "Build a REST API for a blog platform")
`
Code Generation
Python Function
`python
@guidance
def generate_python_function(lm, description):
"""Generate Python function from description."""
lm += f"Description: {description}\n\n"
# Function signature
lm += "def " + gen("func_name", regex=r"[a-z_][a-z0-9_]*") + "("
lm += gen("params", regex=r"[a-z_][a-z0-9_](, [a-z_][a-z0-9_])*") + "):\n"
# Docstring
lm += ' """' + gen("docstring", stop='"""', max_tokens=100) + '"""\n'
# Function body
lm += " " + gen("body", stop="\n", max_tokens=200) + "\n"
return lm
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = generate_python_function(lm, "Check if a number is prime")
print(lm)
`
SQL Query
`python
@guidance
def generate_sql(lm, description):
"""Generate SQL query from description."""
lm += f"Description: {description}\n\n"
lm += "SQL Query:\n"
# SELECT clause
lm += "SELECT " + gen("select_clause", stop=" FROM", max_tokens=100)
# FROM clause
lm += " FROM " + gen("from_clause", stop=" WHERE", max_tokens=50)
# WHERE clause (optional)
lm += " WHERE " + gen("where_clause", stop=";", max_tokens=100) + ";"
return lm
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = generate_sql(lm, "Get all users who signed up in the last 30 days")
`
API Endpoint
`python
@guidance
def generate_api_endpoint(lm, description):
"""Generate REST API endpoint."""
lm += f"Description: {description}\n\n"
# HTTP method
lm += "Method: " + select(["GET", "POST", "PUT", "DELETE"], name="method") + "\n"
# Path
lm += "Path: /" + gen("path", regex=r"[a-z0-9/-]+", stop="\n") + "\n"
# Request body (if POST/PUT)
if lm["method"] in ["POST", "PUT"]:
lm += "\nRequest Body:\n"
lm += "{\n"
lm += ' "field1": ' + gen("field1", regex=r'"[a-z_]+"') + ",\n"
lm += ' "field2": ' + gen("field2", regex=r'"[a-z_]+"') + "\n"
lm += "}\n"
# Response
lm += "\nResponse (200 OK):\n"
lm += "{\n"
lm += ' "status": "success",\n'
lm += ' "data": ' + gen("response_data", max_tokens=100) + "\n"
lm += "}\n"
return lm
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm = generate_api_endpoint(lm, "Create a new blog post")
`
Production Tips
Error Handling
`python
@guidance
def safe_extraction(lm, text):
"""Extract with fallback handling."""
try:
lm += f"Text: {text}\n"
lm += "Name: " + gen("name", regex=r"[A-Za-z ]+", stop="\n", max_tokens=30)
return lm
except Exception as e:
# Fallback to less strict extraction
lm += f"Text: {text}\n"
lm += "Name: " + gen("name", stop="\n", max_tokens=30)
return lm
`
Caching
`python
from functools import lru_cache
@lru_cache(maxsize=100)
def cached_generation(text):
"""Cache LLM generations."""
lm = models.Anthropic("claude-sonnet-4-5-20250929")
lm += f"Analyze: {text}\n"
lm += "Sentiment: " + select(["positive", "negative", "neutral"], name="sentiment")
return lm["sentiment"]
First call: hits LLM
result1 = cached_generation("This is great!")
Second call: returns cached result
result2 = cached_generation("This is great!") # Instant!
`
Monitoring
`python
import time
@guidance
def monitored_generation(lm, text):
"""Track generation metrics."""
start_time = time.time()
lm += f"Text: {text}\n"
lm += "Analysis: " + gen("analysis", max_tokens=100)
elapsed = time.time() - start_time
# Log metrics
print(f"Generation time: {elapsed:.2f}s")
print(f"Output length: {len(lm['analysis'])} chars")
return lm
`
Batch Processing
`python
def batch_process(texts, batch_size=10):
"""Process texts in batches."""
lm = models.Anthropic("claude-sonnet-4-5-20250929")
results = []
for i in range(0, len(texts), batch_size):
batch = texts[i:i+batch_size]
for text in batch:
lm += f"Text: {text}\n"
lm += "Sentiment: " + select(
["positive", "negative", "neutral"],
name=f"sentiment_{i}"
) + "\n\n"
results.extend([lm[f"sentiment_{i}"] for i in range(len(batch))])
return results
`