Prompt chaining

Prompt chaining responds to the fact that complex tasks often cannot be solved well with a single instruction. Instead, the process is split into a sequence of steps, where the output of one prompt becomes the input to the next — for example, the model first extracts the key facts from a document, then writes a summary based on them, and finally formats it for a chosen channel. Each stage does one thing, so it is easier to test and fix independently of the rest.

It is worth drawing a clear line between this concept and a chain of thought. A chain of thought is the laying out of successive reasoning steps within a single answer from one model. Prompt chaining, by contrast, is many separate model calls strung into a flow, where you can insert validation, a tool call or a conditional decision between steps. The first is about reasoning within one answer; the second is about the architecture of the whole process.

In business applications, prompt chaining is often the backbone of simpler automations and the starting point for an agentic workflow. The difference lies in control: in prompt chaining the order of steps is designed up front by a human, whereas in a full agentic workflow the model itself more often decides which step to take next. That is why prompt chaining is predictable and easy to audit — qualities that are prized wherever repeatable results matter.