Prompt Chaining: Breaking Complex Tasks
Split large tasks into sequential steps for dramatically better results.
Why Chain Prompts?
Complex tasks fail as single prompts because:
- The AI tries to do too much at once and cuts corners
- Quality drops when juggling multiple requirements
- Errors in early steps cascade through the entire output
Instead of: "Write a complete marketing strategy for my SaaS product"
Chain it:
1. "Analyze my target audience based on this product description"
2. "Based on that audience, identify the 5 most effective marketing channels"
3. "For each channel, create a 30-day content calendar"
4. "Write the first week's content for the top 2 channels"
Each step builds on the previous one, and you can course-correct between steps.
Common Chaining Patterns
Research → Analyze → Create: Gather information, analyze it, then produce output based on analysis.
Draft → Review → Refine: Generate initial content, critique it, then improve based on the critique.
Plan → Execute → Verify: Create a plan, execute each part, then verify the result.
Decompose → Solve → Synthesize: Break a complex problem into parts, solve each part, then combine the solutions.
Take a complex task you need done (writing a report, planning an event, building a strategy) and break it into 4-5 chained prompts. Execute each one sequentially, reviewing between steps.
- ✓Complex tasks produce better results when split into sequential prompts
- ✓Each step should build on the output of the previous step
- ✓Review and course-correct between steps
- ✓Common patterns: Research→Analyze→Create, Draft→Review→Refine