What is prompt chaining?
Prompt chaining is a technique where you split a complex task into several smaller prompts and pass the result of one step into the next. Rather than asking a language model to do everything in a single request, you build a short pipeline: extract the facts, then summarize them, then draft the email. Each step does one job, and the chain produces a better final answer than one overloaded prompt.
In plain words
It works like an assembly line. One station cuts the parts, the next assembles them, the last paints them. Each worker focuses on a single task and hands off clean work. Trying to do all three at once, in one motion, is how mistakes creep in.
When to use it
- Multi-step tasks. When a job naturally breaks into stages, like research, then analysis, then writing, give each stage its own prompt.
- When one prompt gives muddled results. If a single request keeps half-finishing the work, splitting it usually fixes the quality.
- When you need checkpoints. Between steps you can validate, correct, or branch before continuing, which is hard inside one big prompt.
- For reuse. A well-defined step can be reused across different chains.
Common pitfalls
- Errors compound. A mistake early in the chain flows into every later step. Validate intermediate outputs where it matters.
- Too many links. Each step adds latency and cost. Chain only as much as the task actually needs.
- Losing context between steps. Each prompt only knows what you pass it. If a later step needs earlier context, include it explicitly.
Related articles:
- What is prompt engineering? - Writing the individual prompts in your chain well.
- What is chain-of-thought prompting? - Getting a model to reason step by step inside one prompt.
- What is agentic AI? - When chains become autonomous agents that decide their own steps.
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