Complex tasks rarely fit in a single prompt. Prompt chaining breaks a complex task into a pipeline of simpler, focused prompts where each step's output feeds into the next.

Why Chain Instead of One Big Prompt?

• Better quality — Each step focuses on one thing
• Easier debugging — You can inspect intermediate results
• More reliable — Simpler prompts fail less often
• Flexible — Swap out individual steps without rewriting everything

The Pipeline Pattern

Example: Analyzing a long document for business insights.

Step 1: Extract — "Read this document and extract all mentioned metrics, dates, and key claims. Return as a structured list."

Step 2: Categorize — "Given these extracted items, categorize each as: Financial, Operational, Strategic, or Risk-related."

Step 3: Analyze — "For each category, identify the top 3 most significant findings and explain their business implications."

Step 4: Synthesize — "Given this analysis, write a one-page executive summary with recommendations."

Gate Patterns

Add decision points between steps:

1. Generate a plan
2. Gate: Evaluate the plan — "Does this plan address all requirements? List any gaps."
3. If gaps exist, loop back to step 1 with the feedback
4. Execute the approved plan

Parallel Chains

Some steps can run in parallel:

• Chain A: Analyze financial data
• Chain B: Analyze customer feedback
• Chain C: Analyze competitive landscape
• Merge: Synthesize all three analyses into a unified report

Error Handling in Chains

Each step should include validation:

"If the input doesn't contain enough information to complete this step, return: { 'status': 'insufficient_data', 'missing': ['list of what's needed'] }"

This lets you catch failures early rather than propagating garbage through the pipeline.

Real-World Applications

• Content creation — Research → Outline → Draft → Edit → Format
• Data analysis — Extract → Clean → Analyze → Visualize → Report
• Code generation — Requirements → Architecture → Implement → Test → Review
• Customer support — Classify → Route → Draft response → Quality check