Chris Creates with AI
Back to Techniques
Advanced9 min readPrompting Techniques

October 20, 2024

Connect multiple prompts for complex workflows and build sophisticated AI pipelines.

Prompt Chaining

Prompt chaining is a powerful technique that breaks down complex tasks into sequential steps, where the output of one prompt becomes the input for the next. This approach enables you to build sophisticated AI workflows that handle multi-stage processes efficiently.

What is Prompt Chaining?

Prompt chaining involves connecting multiple prompts in sequence, where:

  • Each prompt handles a specific subtask
  • Output from one prompt feeds into the next
  • Complex tasks are broken into manageable steps
  • Each step can be optimized independently

Think of it as an assembly line for AI tasks, where each station (prompt) performs a specific operation before passing the work to the next station.

Why Use Prompt Chaining?

Better Results

Breaking complex tasks into steps allows for more focused, higher-quality outputs at each stage.

Easier Debugging

When something goes wrong, you can identify exactly which step failed and fix just that prompt.

Reusability

Individual prompts in the chain can be reused in different workflows.

Specialization

Each prompt can be optimized for its specific subtask without worrying about the entire process.

Manageability

Complex tasks become manageable when broken into clear, sequential steps.

How Prompt Chaining Works

Basic Structure

Input Data
    ↓
[Prompt 1] → Output 1
    ↓
[Prompt 2] → Output 2
    ↓
[Prompt 3] → Final Result

Example: Content Creation Pipeline

Step 1 - Research:

Prompt: "Research and list 5 key benefits of remote work based on recent studies"
Output: List of 5 benefits with sources

Step 2 - Outline:

Prompt: "Create a blog post outline using these benefits: [Output from Step 1]"
Output: Structured outline with introduction, main points, conclusion

Step 3 - Draft:

Prompt: "Write a 500-word blog post following this outline: [Output from Step 2]"
Output: Complete draft article

Step 4 - Optimize:

Prompt: "Optimize this article for SEO, suggest meta description and title: [Output from Step 3]"
Output: Final article with SEO elements

Types of Prompt Chaining

Sequential Chaining

Each step follows the previous in a straight line.

Step 1 → Step 2 → Step 3 → Result

Use for: Linear workflows with clear dependencies

Parallel Chaining

Multiple chains run simultaneously and combine at the end.

      → Chain A →
Input                 → Combine → Result
      → Chain B →

Use for: Tasks requiring multiple perspectives or approaches

Conditional Chaining

Branches based on conditions or outputs.

Step 1 → Decision →  Path A → Result A
                 →  Path B → Result B

Use for: Workflows with different paths based on criteria

Iterative Chaining

Loops back to refine outputs.

Step 1 → Step 2 → Evaluate → (if not good enough) → back to Step 1
                          → (if good) → Result

Use for: Quality-driven outputs requiring refinement

Practical Applications

Research and Analysis

Chain:
1. Extract key themes from document
2. Analyze each theme separately
3. Identify connections between themes
4. Synthesize findings
5. Generate recommendations

Content Translation and Localization

Chain:
1. Translate text to target language
2. Adapt cultural references
3. Adjust tone for local audience
4. Check for idiomatic correctness
5. Final polish and format

Data Processing

Chain:
1. Clean and format raw data
2. Extract relevant fields
3. Categorize information
4. Generate summary statistics
5. Create visualization descriptions

Code Development

Chain:
1. Write function specification
2. Generate initial code
3. Add error handling
4. Write unit tests
5. Add documentation
6. Optimize performance

Best Practices

1. Clearly Define Subtasks

Each prompt in the chain should have a single, well-defined purpose.

Poor:

"Analyze this data and write a report with recommendations"

Better:

Step 1: "Analyze this data and identify key trends"
Step 2: "Based on these trends: [output], list implications"
Step 3: "From these implications: [output], generate recommendations"

2. Format Outputs Carefully

Ensure the output of one prompt aligns with the input requirements of the next.

Example:

Prompt 1 Output Format: JSON with specific fields
Prompt 2 Input Expectation: JSON with those exact fields

3. Include Context

Pass necessary context through the chain.

Prompt 2: "Based on the analysis from Step 1: [full context], and considering the original goal of [goal], proceed with..."

4. Implement Error Handling

Build in checks to catch issues early.

After Step 2: "Verify that the output contains all required elements before proceeding to Step 3"

5. Optimize Each Step

Test and refine individual prompts before chaining them.

Implementation Strategies

Manual Chaining

You run each prompt manually and copy outputs to the next prompt.

Pros: Full control, easy to inspect each step Cons: Time-consuming, prone to copy-paste errors

Automated Chaining

Use scripts or tools to automate the process.

Example (Python pseudocode):

def chain_prompts(input_data):
    # Step 1
    result_1 = ai_call(prompt_1, input_data)
    
    # Step 2
    result_2 = ai_call(prompt_2, result_1)
    
    # Step 3
    final_result = ai_call(prompt_3, result_2)
    
    return final_result

Tool-Assisted Chaining

Use AI platforms that support prompt chaining (LangChain, Make, Zapier, etc.)

Real-World Example: Customer Support Response

The Workflow

Step 1 - Classify:

Input: Customer email
Prompt: "Classify this customer inquiry as: Technical, Billing, General, or Complaint"
Output: "Technical"

Step 2 - Extract Details:

Input: Original email + Classification
Prompt: "Extract key technical details: product, issue description, error messages"
Output: Structured data about the issue

Step 3 - Find Solution:

Input: Technical details
Prompt: "Based on these technical details, search our knowledge base and provide relevant solutions"
Output: List of potential solutions

Step 4 - Draft Response:

Input: Solutions + Customer's original tone
Prompt: "Draft a professional, empathetic response including these solutions, matching the customer's communication style"
Output: Draft email

Step 5 - QA Check:

Input: Draft response
Prompt: "Review this response for accuracy, tone, and completeness. Suggest improvements if needed"
Output: Final response or improvement suggestions

Common Patterns

Extract → Transform → Load (ETL)

  1. Extract information from source
  2. Transform to desired format
  3. Load into final structure

Research → Analyze → Synthesize

  1. Gather relevant information
  2. Analyze each piece
  3. Combine into cohesive output

Generate → Critique → Refine

  1. Create initial output
  2. Critically evaluate it
  3. Improve based on feedback

Plan → Execute → Verify

  1. Create detailed plan
  2. Execute each step
  3. Verify results

Challenges and Solutions

Challenge 1: Context Loss

Problem: Important context gets lost as you move through the chain

Solution: Explicitly pass critical context at each step

"Considering the original goal of [goal] and the context that [context], using the results from the previous step..."

Challenge 2: Error Propagation

Problem: An error in an early step cascades through the chain

Solution: Add validation steps

After each critical step: "Verify that [expected output] is present before proceeding"

Challenge 3: Prompt Drift

Problem: Later prompts don't align with earlier outputs

Solution: Test the entire chain, not just individual prompts

Challenge 4: Token Limits

Problem: Passing large outputs through chains hits token limits

Solution: Summarize or extract only essential information between steps

Advanced Techniques

Branching and Merging

Input → Analyze → Branch to specialists → Merge insights → Output

Feedback Loops

Generate → Evaluate → (if score < threshold) → Improve → Evaluate → ...

Dynamic Chaining

Adjust the chain based on intermediate results.

Step 1 → Decision: if complex → add extra analysis steps
                   if simple → proceed directly to output

Measuring Success

Metrics to Track

  • Completion Rate: % of chains that complete successfully
  • Quality Score: Ratings of final outputs
  • Time Efficiency: Time saved vs. manual process
  • Error Rate: % of chains with errors
  • User Satisfaction: Feedback on results

Tips for Effective Chaining

  1. Start Simple: Begin with 2-3 step chains before building complex workflows
  2. Document Each Step: Write clear descriptions of each prompt's purpose
  3. Test Thoroughly: Run the full chain multiple times with different inputs
  4. Monitor Outputs: Regularly check that intermediate outputs are as expected
  5. Version Control: Track changes to your prompt chain over time
  6. Measure Performance: Benchmark each step for speed and quality
  7. Plan for Failures: Build in fallbacks for when steps fail

Conclusion

Prompt chaining transforms simple AI interactions into powerful, multi-stage workflows. By breaking complex tasks into manageable steps, you gain:

  • Better control over outputs
  • Easier debugging and optimization
  • Reusable components
  • More sophisticated capabilities

Start with simple chains, master the fundamentals, then gradually build more complex workflows. With practice, you'll be able to design sophisticated AI pipelines that handle even the most complex tasks efficiently.

Next Steps:

  • Identify a complex task you currently do manually
  • Break it into 3-5 clear steps
  • Create a prompt for each step
  • Test the chain and refine
  • Automate if beneficial

Prompt chaining is essential for anyone serious about leveraging AI for complex, real-world applications.

All TechniquesExplore Guides