Lesson 10: Prompt Chaining & Sequential Processing

The Power of Prompt Chaining

Single prompts have limitations — they can only process so much information and produce so much output. Prompt chaining breaks complex tasks into sequential steps, where each step's output becomes the next step's input.

Why Chain Prompts?

Mental model: Think of prompt chaining like an assembly line — each station does one thing well, and the product gets refined as it moves down the line.

Basic Chaining Patterns

Pattern 1: Linear Chain

The simplest pattern — output of A feeds into B, B into C, etc.

Step 1: Research → Generate outline
Step 2: Outline → Write first draft
Step 3: Draft → Edit and refine
Step 4: Refined → Add formatting and polish

Example: Blog Post Creation

Chain Step 1:
"Research the topic: 'AI in Healthcare 2026'
Provide: 5 key trends, supporting data, notable examples"

[Output: Research findings]

Chain Step 2:
"Based on this research: [Step 1 output]
Create a blog post outline with:
- 5 main sections based on the trends
- Key points for each section
- Suggested headlines"

[Output: Structured outline]

Chain Step 3:
"Using this outline: [Step 2 output]
Write a complete 1500-word blog post.
Include: Introduction, detailed sections, conclusion with CTA"

[Output: First draft]

Chain Step 4:
"Edit this draft for:
- Grammar and clarity
- Engagement and flow
- SEO optimization
- Consistency in tone

Draft: [Step 3 output]"

[Output: Final polished post]

Pattern 2: Branching Chain

Split into parallel paths, then merge results.

Step 1: Analyze input → Identify components
Step 2a: Process component A
Step 2b: Process component B  
Step 2c: Process component C
Step 3: Merge all results → Final output

Example: Competitive Analysis

Step 1: "List the top 5 competitors for [product]"

Step 2a: "Analyze Competitor 1: [name]"
Step 2b: "Analyze Competitor 2: [name]"
Step 2c: "Analyze Competitor 3: [name]"
(Run in parallel)

Step 3: "Based on these competitor analyses:
[Results from 2a, 2b, 2c]
Create a comparative analysis with:
- Feature comparison table
- Strengths/weaknesses matrix
- Market positioning map
- Strategic recommendations"

Pattern 3: Iterative Refinement

Repeat a step until quality criteria are met.

Step 1: Generate initial output
Step 2: Evaluate against criteria
Step 3: If quality < threshold, refine and repeat Step 2
Step 4: Output final result

Example: Code Generation

Iteration 1:
"Write a Python function that [specification]"

Iteration 2:
"Review this code for:
- Correctness
- Edge cases
- Performance
- Readability

Code: [Iteration 1 output]

Identify issues and provide improved version."

Iteration 3:
"Final review:
- Are all edge cases handled?
- Is error handling robust?
- Is the code production-ready?

Code: [Iteration 2 output]

If issues remain, fix them. Otherwise, confirm it's ready."

Chain Design Principles

1. Define Clear Interfaces

Each step should have clear inputs and outputs:

Step 1 Output Format:
{
  "findings": ["list of insights"],
  "confidence": "high/medium/low",
  "sources": ["list of sources"]
}

Step 2 Input Requirements:
- findings: list of strings
- confidence: string
- sources: list of strings (optional)

2. Minimize Dependencies

When possible, make steps independent so they can run in parallel:

# Bad: Each step depends on previous
Step 1 → Step 2 → Step 3 → Step 4

# Better: Independent steps can parallelize
Step 1 → Step 2a ↘
Step 1 → Step 2b → Step 3
Step 1 → Step 2c ↗

3. Include Validation Checkpoints

Add quality gates between steps:

Step 1: Generate outline
[VALIDATION: Does outline cover all requirements?]

Step 2: Write content
[VALIDATION: Is content accurate and complete?]

Step 3: Edit and polish
[VALIDATION: Does final meet quality standards?]

4. Handle Errors Gracefully

Plan for failures at each step:

Step 1: Generate content
If Step 1 fails:
  - Log the error
  - Retry once with simplified requirements
  - If still fails, output partial results with explanation

Complex Workflow Example: Content Pipeline

INPUT: Blog topic + target audience

Step 1: Research
- Generate 5 key points
- Find supporting data
- Identify examples
OUTPUT: Research brief

Step 2: Outline
- Create section structure
- Assign key points to sections
- Write section headers
OUTPUT: Detailed outline

Step 3: Draft (parallel per section)
- Section 1 draft
- Section 2 draft
- Section 3 draft
OUTPUT: Raw sections

Step 4: Integrate
- Combine sections
- Add transitions
- Ensure flow
OUTPUT: Complete draft

Step 5: Optimize
- SEO optimization
- Readability check
- Engagement enhancement
OUTPUT: Optimized draft

Step 6: Polish
- Grammar check
- Formatting
- Final review
OUTPUT: Publication-ready post

Common Chaining Mistakes

• Mistake: Chains too long (10+ steps) — Fix: Consolidate steps or break into sub-chains.
• Mistake: No validation between steps — Fix: Add quality checkpoints.
• Mistake: Tight coupling between steps — Fix: Define clear interfaces.
• Mistake: Not handling failures — Fix: Plan error recovery for each step.
• Mistake: Passing too much context — Fix: Summarize between steps when possible.

Professional Tips & Tricks

• Start with simple 2-3 step chains, then expand as needed.
• Document your chains — they become reusable workflows.
• Use version control for prompt chains — track what works.
• Test chains with edge cases, not just happy paths.

Key Takeaways

• Prompt chaining breaks complex tasks into manageable steps.
• Linear, branching, and iterative are the main chain patterns.
• Define clear interfaces between steps for maintainability.
• Include validation checkpoints to catch issues early.
• Plan for errors at each step of the chain.

Next up: Orchestrating multiple AI agents and parallel processing.