Prompt chaining is the practice of breaking a complex AI task into a series of smaller prompts, where the output of one step becomes the input to the next. Instead of asking the model to do everything in one breath, you hand it a clear sequence of simpler jobs.
Imagine an assembly line for a piece of writing. Station one reads a long document and pulls out the key points. Station two takes those points and drafts a summary. Station three rewrites that summary in the company’s tone of voice. Each prompt does one thing well, and the partly finished work moves down the line. This is the backbone of many an AI workflow, because small focused steps are easier to get right than one tangled instruction.
The real payoff is debugging. When a single mega-prompt gives a bad answer, you have no idea which part failed. With a chain, you can see exactly which station produced the problem and fix only that step. You can also mix models: a cheap fast one for the simple extraction step, a stronger one for the final rewrite, so you only pay for power where it earns its keep.
There is a catch worth knowing. Errors travel down the chain. If step one misreads the document, every step after it inherits the mistake, so the early links deserve the most testing. A good rule is to validate the output between steps, sometimes with output parsing, rather than trusting that each handoff is clean.
At TopDevs we use prompt chaining to build AI features we can actually trust and maintain, since each link in the chain can be tested and improved on its own.
