Prompt Chaining

We’ve laid down the foundations. You’ve seen how to use tools, return structured output, plug-in retrieval, and even track memory across a conversation. And here’s the big unlock: none of those techniques live in isolation. When we’re building real-world AI systems—agents that plan, write, summarize, or automate— we’re not calling one model once. We’re building a chain of moves. A sequence. A strategy. A pattern.

Think of it like this: in a team-based game, you don’t win by picking the strongest characters in isolation. You draft for synergy. Tank, engage, back line carry—different roles, one coordinated goal. It’s the same with AI components. Tool use, retrieval, output formatting—each one does something specific, but the real power comes when you stack them together in the right order for the task at hand.

It usually starts with a trigger: a user sends a message, a system logs an event, or some new input appears. That input flows into an initial prompt. But often, that one prompt alone won’t get us to the final answer. Maybe we need to draft an outline first, check that it meets a specific criterion, and only then proceed to write the full document. Each step has a different prompt and a different role to play—but together, they form a tightly connected flow. Prompt chaining is the art of linking multiple prompts together, where the output of one becomes the input of the next, creating a cohesive, multi-step reasoning pipeline.

In this lesson, we’ll walk through a clear example: generating a document by first outlining it, validating that outline through specific criteria, and only then producing the full content. Along the way, you’ll see how chaining helps break a complex task into smaller, safer steps. Each link in the chain adds structure, and the result is something you can trust.

When to use prompt chaining?

Prompt chaining is perfect when your task has structure, in the form of writing, analysis, decision-making, or long-form workflows. Instead of asking the model to juggle everything in one go, you break the task into smaller subtasks, where the output of one step becomes the input for the next. Maybe it starts with a headline, then moves to a bullet-point outline, then drafts a blog post. Each call becomes more focused, easier to check, and way less prone to drift. It’s a system built for clarity and consistency.

You also reach for this agentic design pattern when precision matters more than speed, and when the overall job has natural phases. Say you’re building a meeting-booking agent: first, detect if the input even refers to a meeting. If yes, extract attendees, time, and place. If all details are present, move to scheduling; if not, follow up or terminate execution. Another example? Event summarization: detect the event, draft a summary, and then check if it meets the word count, and tone guidelines. Each step is easier to debug, and you’re always in control.

The real win here is combining model flexibility with logical scaffoldingIt's the rigid, rule-based structure you impose on a flexible AI model to turn its chaotic potential into a predictable, step-by-step process.. With prompt chaining, you get to act like a coach drawing up plays between each possession. You don’t just watch the model take the shot and pray. You create structured hand-offs, add checks ...