What Is Prompt Chaining?

Prompt chaining is breaking a complex task into a sequence of prompts, where the output of one becomes the input to the next. Instead of asking a model to do everything in one step, each prompt handles a piece, and the steps are linked so the results build toward the final answer.

How prompt chaining works

Prompt chaining splits a job into stages and runs a separate prompt for each, feeding results forward. To summarize and then translate a document, for instance, one prompt produces the summary and a second takes that summary and translates it. Each step is simpler and more reliable than trying to do both at once, and the chain connects them into a complete workflow.

Because each stage is its own prompt, the steps can be checked, reused, and adjusted independently. This makes chaining a practical way to handle tasks that are too involved for a single prompt, and it is a common pattern inside AI agents and orchestrated systems that carry out multi-step work.

Why prompt chaining matters for AI

Prompt chaining matters because breaking work into steps usually beats asking for everything at once. A model handling one focused instruction at a time tends to be more accurate and easier to debug than one juggling a large, tangled request. It also lets a system insert checks or use different tools between steps. At Custom AI Studio, chaining prompts into clear stages is one way we build workflows that stay reliable as tasks get more complex.