How to chain Claude calls to build multi-step reasoning pipelines
- Authors
- Name
- ThePromptEra Editorial
You've hit a wall. Your prompt is getting bloated. Claude's returning partial answers. You need something that works like a real system, not just a single conversation turn.
Welcome to prompt chaining—the professional's answer to complex reasoning problems.
Why single prompts fail at complex tasks
Before we dive into solutions, let's acknowledge the problem. When you throw a multi-step problem at Claude in one prompt, you're asking it to:
- Parse the entire problem space
- Maintain context across reasoning steps
- Make intermediate decisions
- Correct course if early steps went sideways
- Deliver a polished final output
All at once. In one forward pass.
This works fine for moderately complex tasks. But when you need genuine multi-step reasoning—analysis that depends on previous results, decision trees that branch based on findings, or synthesis that requires multiple perspectives—you're fighting Claude's architecture, not leveraging it.
Chaining changes this. Instead of asking Claude to do everything perfectly in one shot, you decompose the problem into discrete steps, execute them sequentially, and feed each output into the next input.
The basic chain architecture
Here's the simplest useful pattern:
import anthropic
client = anthropic.Anthropic()
# Step 1: Analyze the problem
analysis = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[
{
"role": "user",
"content": f"Analyze this customer complaint and identify the root cause:\n\n{complaint}"
}
]
)
analysis_text = analysis.content[0].text
# Step 2: Generate a solution based on the analysis
solution = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[
{
"role": "user",
"content": f"Based on this analysis:\n\n{analysis_text}\n\nGenerate a customer response that addresses the root cause."
}
]
)
solution_text = solution.content[0].text
# Step 3: Review for tone and accuracy
final = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[
{
"role": "user",
"content": f"Review this response for professional tone and accuracy:\n\n{solution_text}\n\nMake any necessary improvements."
}
]
)
print(final.content[0].text)
This three-step chain lets Claude focus on one job per call. Each step is cleaner, faster, and more reliable than asking it to do all three at once.
## Routing logic: making chains intelligent
Real chains need decision points. Not every problem takes the same path.
```python
def classify_issue(complaint):
"""Determine issue type to route to appropriate handler"""
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=100,
messages=[
{
"role": "user",
"content": f"Classify this issue as BILLING, TECHNICAL, or SERVICE:\n\n{complaint}"
}
]
)
return response.content[0].text.strip().upper()
def handle_billing_issue(complaint, analysis):
"""Specialized handler for billing problems"""
return client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[
{
"role": "user",
"content": f"You are a billing specialist. Handle this:\n\n{analysis}"
}
]
)
def handle_technical_issue(complaint, analysis):
"""Specialized handler for technical problems"""
return client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[
{
"role": "user",
"content": f"You are a technical support expert. Solve this:\n\n{analysis}"
}
]
)
# Main chain with routing
issue_type = classify_issue(complaint)
analysis = analyze_complaint(complaint)
if issue_type == "BILLING":
solution = handle_billing_issue(complaint, analysis)
elif issue_type == "TECHNICAL":
solution = handle_technical_issue(complaint, analysis)
else:
solution = handle_service_issue(complaint, analysis)
```
Now your chain adapts. The first call determines what the second call should be. This is closer to actual reasoning—decisions inform the next step.
## Practical example: Content generation pipeline
Let's build something real. A content quality pipeline that generates, refines, and validates articles:
```python
def generate_outline(topic, audience):
"""Step 1: Create structure"""
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[
{
"role": "user",
"content": f"Create a detailed outline for an article about {topic} for {audience}."
}
]
)
return response.content[0].text
def write_sections(outline, topic):
"""Step 2: Expand outline into full sections"""
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=2048,
messages=[
{
"role": "user",
"content": f"Write full sections for this outline about {topic}:\n\n{outline}"
}
]
)
return response.content[0].text
def check_depth(content, topic):
"""Step 3: Identify missing depth"""
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=512,
messages=[
{
"role": "user",
"content": f"What key subtopics of {topic} are missing or underdeveloped in this content?\n\n{content}"
}
]
)
return response.content[0].text
def deepen_content(content, gaps):
"""Step 4: Expand missing areas"""
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[
{
"role": "user",
"content": f"Expand this content to address these gaps:\n\n{gaps}\n\nContent:\n{content}"
}
]
)
return response.content[0].text
# Run the chain
outline = generate_outline("prompt engineering", "software engineers")
sections = write_sections(outline, "prompt engineering")
gaps = check_depth(sections, "prompt engineering")
final_content = deepen_content(sections, gaps)
print(final_content)
```
Each step gets better output because it's building on focused, validated work from the previous step.
## Key patterns that actually work
**Keep state minimal.** Pass only what the next step needs. Extra context creates noise.
**Use structured outputs when routing.** The classification step should return a clean category, not prose. Makes routing bulletproof.
**Cache aggressive parts.** If you're running the same analysis multiple times, cache that step's system prompt and it'll run faster on retries.
**Error handling matters.** Check token counts. Some chains will exceed limits if you're not watching. Add fallbacks for when a step fails.
**Log everything.** Chain execution is harder to debug. Store each step's input and output. You'll need it.
## When NOT to chain
Some problems genuinely don't need chains. Simple classification, summarization, or single-turn generation? Keep it simple. Chains add latency and complexity. Use them when you need:
- Conditional logic based on intermediate results
- Specialization (different prompts for different branches)
- Quality gates (checking output before proceeding)
- Iterative refinement (multiple passes improving results)
Without these, you're chaining for the sake of it.
## The real advantage
Chains aren't just about accuracy—though they're usually more accurate. They're about _controllability_. You can test each step independently. You can swap implementations. You can add guardrails at specific points. You can debug exactly where things went wrong.
That's professional-grade AI work. Build your chains deliberately, keep them simple, and let Claude do its best thinking at each step instead of trying to do everything at once.
```