Lesson 6 of 8
Chain the steps
6 min read
You ask for the whole thing at once — "plan my entire five-day trip, with budget, hotels, and a daily schedule" — and get back something shallow and generic. Break that same job into a few steps and the results transform.
One giant ask versus a chain
A big, do-everything prompt forces the AI to juggle every part at once, and it ends up doing none of them well. Chaining means splitting the job into a short sequence of prompts, where each answer feeds the next: first ask for the top sights, then have it group those into days, then flesh out one day in detail. Each step is small and focused, so each result is sharp — and they build on each other.
Breaking a big task into a sequence of smaller prompts — each one's output feeding the next — beats a single giant request. One focused question at a time gives the AI room to do each part well.
Catch mistakes before they spread
Chaining has a second payoff: you see each step's result before moving on, so you can catch a wrong turn early — before it poisons everything downstream. That's also its main risk: if a mistake slips through an early step, every later step builds on it. So glance at each result before you continue, and keep chains reasonably short — a handful of steps, not twenty.
Because each step feeds the next, you can catch an error early — but a mistake that slips through corrupts everything after it. Check each result, and keep the chain short.
You're already doing a simple version of this whenever you reply "good, now make it shorter" — that follow-up is the next link in a chain. Chaining is just doing it deliberately, from the start.
The gist
- —A short sequence of focused prompts beats one giant do-everything request
- —Each step's output feeds the next, so the plan builds up instead of arriving flat
- —Check each result as you go — an early mistake spreads to every step after it
You want a polished blog post on a topic you barely know. Which approach works best?
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