Prompt Chaining

A workflow pattern where multiple prompts are linked together so the output of one step becomes the input to the next.

Prompt chaining is the practice of splitting a complex task into multiple AI steps instead of asking for everything in one prompt. Each step has a narrower objective, and its output feeds into the next prompt in the chain.

For example, a workflow might first extract facts from a document, then classify them, then generate a summary, then rewrite that summary for a specific audience. Breaking tasks apart often improves reliability, traceability, and control.

Why teams use it: Smaller prompts are easier to debug, easier to evaluate, and often more accurate than one giant prompt handling many responsibilities at once.

Common Uses of Prompt Chaining

Content workflows — research, outline, draft, and edit in stages
Document analysis — extract, classify, then summarize
Agent systems — plan first, then act on each step
Structured automation — combine prompts with validators and tools

Prompt chaining is often a lightweight alternative to building a full AI agent. It is especially useful when you want predictable workflows without giving the model too much autonomy.

Related Terms

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