Prompt chaining: Prompt chaining is the practice of breaking a complex task into a sequence of smaller prompts, where the output of one step becomes the input to the next, instead of asking an AI model to do everything in a single request.
A single prompt that asks for research, analysis, a draft, and a polish all at once tends to produce something mediocre at every stage. Prompt chaining splits the job into steps you can inspect: first extract the facts, then organize them, then write, then edit. Each step gets a focused prompt, and you (or an automation) pass the result along.
The payoff is control. When the final result is wrong, you can see which link failed and fix only that one. You can also use different settings at different steps: a careful, low-temperature prompt for pulling out numbers and a looser one for drafting the narrative. In tools like Zapier, Make, or n8n, each link of the chain becomes its own step, which is how many workplace automations are built.
The cost is time and tokens. A chain of five prompts means five calls, and errors compound if an early step produces something subtly wrong that later steps trust. Add a checkpoint where a person reviews the intermediate output before the chain continues, especially for anything customer-facing.
Example at work
An operations manager wants a weekly vendor performance summary. Prompt one pulls late-delivery counts from a pasted report into a clean table. Prompt two ranks vendors and flags anyone below the agreed service level. Prompt three turns the flagged list into a short email to procurement. She checks the table after step one, because if the counts are wrong, everything downstream is wrong too.
Why it matters
Most work that feels too big for AI is really several small tasks stacked together. Chaining lets you get reliable results on each piece, catch mistakes early, and turn a one-off chat into a repeatable process that a colleague or an automation tool can run.
Related terms
- PromptA prompt is the text (and sometimes files or images) you give an AI model to tell it what you want. A good prompt states the role the AI should play, the task, the relevant context, the output format, and any constraints.
- Prompt engineeringPrompt engineering is the practice of writing and refining instructions to get reliable, high-quality output from an AI model. It covers giving context, showing examples, specifying format, and iterating on what did not work.
- Workflow automationWorkflow automation connects apps and steps so that a trigger, such as a new form response or an incoming email, automatically runs a sequence of actions, increasingly with an AI step that reads, decides, or writes along the way.
- Chain-of-thought promptingChain-of-thought prompting asks an AI model to work through a problem step by step before giving its final answer. Showing its reasoning tends to improve accuracy on math, logic, and multi-step tasks and makes mistakes easier to spot.
- AI agentAn AI agent is a system that uses an AI model to pursue a goal on its own by planning steps, using tools such as web browsers, files, and software APIs, and adjusting based on results, rather than answering a single prompt.