What is Prompt Chaining?

Prompt Chaining is when you break a complex task into multiple AI prompts where the output of one prompt becomes the input for the next. Instead of asking the AI to "write a blog post," you chain: 1) Generate outline, 2) Write intro from outline, 3) Write body sections, 4) Write conclusion, 5) Edit for tone. Each step is simpler, more reliable, and easier to debug than one mega-prompt.

When Should You Use This?

Use prompt chaining for complex, multi-step workflows where a single prompt would be too complicated or unreliable. Examples: content generation pipelines, data processing (extract → transform → summarize), code generation (plan → write → test → refactor), research tasks (search → summarize → synthesize). Especially useful when you need to inspect/validate intermediate outputs before proceeding.

Common Mistakes to Avoid

• •Not validating intermediate steps—garbage in, garbage out; check each step before chaining
• •Chaining too many steps—every step adds latency and cost; sometimes one good prompt is better
• •Losing context between steps—pass relevant info forward, don't assume AI remembers earlier steps
• •Not handling failures—if step 3 fails, what happens? Build error handling and retries
• •Over-engineering simple tasks—not everything needs a chain; start simple, add steps only if needed

Real-World Examples

• •Zapier/Make.com—entire platforms built on chaining AI steps with other tools
• •LangChain—framework specifically for building prompt chains and AI workflows
• •Content agencies—chain: research → outline → draft → edit → SEO optimize → publish
• •Data analysis—chain: SQL query → extract data → summarize findings → generate chart → write report