Complex tasks often fail when attempted in a single prompt. Prompt chaining breaks these tasks into sequential steps, where each prompt builds on previous outputs. This approach provides more control, better results, and the ability to verify each step.
How Prompt Chaining Works
Instead of asking for everything at once, you break the task into steps. First prompt: analyze the problem. Second prompt: generate a solution based on the analysis. Third prompt: refine the solution. Each step uses outputs from previous steps.
This sequential approach allows you to verify each step before proceeding. If an early step produces incorrect results, you can correct it before continuing. This prevents errors from compounding.
When to Use Chaining
Use prompt chaining for complex tasks that require multiple reasoning steps: research projects, multi-part analyses, code development with multiple components, and creative projects requiring planning then execution.
Chaining is also valuable when you need to maintain context across steps. Each prompt can reference previous outputs, building a coherent workflow that maintains consistency throughout the process.
Best Practices
Design clear step boundaries. Each step should have a specific, verifiable output. This makes it easy to check progress and identify where problems occur.
Pass context between steps explicitly. Reference previous outputs clearly: "Based on the analysis from step 1, now generate..." This helps the model maintain continuity.
Allow for iteration. If a step produces unsatisfactory results, you can re-run that step with adjustments before proceeding. This flexibility makes chaining powerful for complex work.
Key Takeaways
- • Break complex tasks into sequential steps
- • Each step builds on previous outputs
- • Verify each step before proceeding
- • Pass context explicitly between steps
- • Allows for iteration and correction