Understanding what chained prompts actually mean
When I first heard “chained prompts” I honestly pictured some sort of command-line thing with pipes and arrows. In reality, it’s just getting GPT to answer in a series of linked questions and answers instead of you dumping everything upfront. For example, I don’t ask it to spit out a fully polished blog post in one go — I ask it first to brainstorm titles, then pick one, then generate an outline, then expand each section. Each step knows about the one before it.
Why do this? Because if you send in one giant prompt, GPT often forgets tiny parts you thought were important. I learned this while watching it totally ignore my product name three times in a row. By chaining, you anchor the model’s focus. It’s like cooking in phases: you prep the veggies, boil the pasta, then make the sauce, instead of trying to do all three in the same pot at once. 🙂
When I’m building a chain, my “Step 1” prompt is usually something like:
“`
List 7 article ideas about [topic] written in a conversational tone. Avoid clickbait.
“`
I paste the results, choose my favorite, then my Step 2 is:
“`
Create an outline for an article titled “[chosen title]”, with casual language and specific examples.
“`
That’s when you start feeling like the conductor instead of the passenger. And yes, sometimes Step 3 inexplicably ignores your outline and decides to invent its own. More on fixing that below.
Setting up your chained prompts in a document
I don’t run these inside the GPT chat window all the time. If I’m working on something longer, I have a Google Doc with prompts labeled like:
– Step 1 Brainstorm hooks
– Step 2 Build outline
– Step 3 Expand section one
– Step 4 Expand section two
Keeping them in a doc is the only way I can avoid asking the model the same exact thing three hours later with that déjà vu feeling. I also leave notes to myself like “don’t forget to mention the webhook bug” because otherwise by Step 4 I’ll space out and realize the most important troubleshooting detail didn’t make it in.
If you want to track versions, add a little table at the top of your doc:
| Step | Prompt summary | Response saved |
|——|————————|—————-|
| 1 | Title ideas | Yes |
| 2 | Outline | Yes |
| 3 | First section draft | No |
| 4 | Second section draft | Yes |
This makes it really obvious if you forgot to copy something over before GPT’s memory in the chat expires. No fun rebuilding a chain because you closed the wrong tab. ¯\_(ツ)_/¯
Dealing with when a step goes off track
The most frustrating thing with chained prompts is the derailment. You tell it in Step 3 to expand your second outline point about a tool bug, and it suddenly writes an essay about generic productivity tips. When that happens, I’ve learned not to regenerate blindly. Instead, I paste the original outline bullet right into the new prompt, like:
“`
Using this exact bullet point: “[paste]”, write 300 words in the same tone as the Step 2 outline.
“`
I also add a quick note in brackets saying “Do NOT invent other topics.” This reinforcement seems to help it stay in lane. If it still wanders off… I actually go back to Step 2 and rewrite that bullet to be unnervingly specific. Something like: “Explain how the webhook from Tool A fired twice in under a minute, causing duplicate invoices, and what temporary manual workaround fixed it.”
The more vivid you make the outline, the less creative GPT tries to get when you don’t want it to.
Keeping context between steps without memory loss
Even though GPT sort of remembers the chat history, I’ve noticed that long chains get fuzzy after maybe 10 messages. It forgets tone notes, skips specific terms. That’s why I copy the needed parts forward in every prompt. Painful? Yes. But you can’t assume it will “just know.”
For example, in a 6-step chain, Step 5 will start like:
“`
Here is the title: “[title]”
Here is the outline bullet we are expanding: “[bullet]”
Here is the tone: conversational, clear, with human side-notes and some examples from real bugs.
“`
Then I paste in the last 100 words of the previous section so it feels continuous. It’s old-school cut-and-paste memory management, basically. I do the same thing when I’m using other AI tools — copy in context manually every step — because otherwise you hit that weird point where it’s suddenly writing as if it’s a Wikipedia editor from 2008.
Automating chains with a zap or script
If you’re feeling fancy, you can set this whole thing up so you don’t need to babysit each step. I once built a Zap that would:
1. Take a Google Doc title
2. Pass it to GPT for Step 1
3. Append results to the doc
4. Trigger Step 2 with the chosen idea
5. Keep going until a full article appeared
It worked beautifully for 48 hours, and then — without any changes — the zap started appending Step 3’s content to the wrong document. I still have no idea why. I fixed it by adding a unique identifier to the beginning of every output and filtering based on that, but the chain still occasionally skips a step if the API times out.
So yes, automation can save you time, but keep an eye on it the first week. You’d be surprised how often a random glitch makes it “write” directly into a doc you haven’t touched in months.
Asking smaller questions for better output
One thing chaining really teaches you is to stop asking huge questions like “Write me a blog post about this software.” Instead, I ask tiny, controlled questions:
– “Give me five analogies for how this process feels in real life.”
– “List three bugs that could break this workflow.”
– “Write a mock customer quote about how it saved time.”
These micro-prompts give me specific building blocks I can arrange later. And honestly, those blocks feel more real because they have detail. When GPT starts vague, it’s almost impossible to drag it back into specificity later.
I even break up examples into their own step. Like:
“`
List just the examples I could use in this section, no prose, no transition sentences.
“`
Then I come back and say:
“`
Now write the section using those examples.
“`
It’s a lot more work upfront, but you stop getting the same recycled intro paragraphs over and over.
Repairing a broken chain mid project
Sometimes Step 4 fails so badly you’re tempted to scrap the whole draft. I used to. Now, I salvage what I can. If Step 4 outputs something way off, I isolate the last good step, copy the correct bullet point, and run that step again with a variation of the prompt.
If GPT really refuses to cooperate, I’ll feed the last working 200 words into a brand new chat and re-initiate the chain from that point. That way, you’re not dragging along 15 messages of potential confusion. It’s like unplugging a frozen modem — yes, you lose some logs, but you get back to a reliable state.
I’ve done this mid-project only to have the regenerated section end up sounding *better* than the first try, so you never know when a derailment might actually improve your post.
Using human edits between steps
Don’t trust any chain to be publish-ready without you in the loop. Between Step 2 and Step 3, I often rewrite the outline bullets, adding notes about tone, examples, or even specific phrases I want used. Between Step 4 and Step 5, I cut fluff manually before the next expansion, so GPT doesn’t get in the habit of padding everything with extra adjectives.
I’ve even gone so far as to feed it my lightly edited Step 3 as context for Step 4 so it keeps the voice consistent. Yes, it’s micromanaging, but better than scrolling later and wondering why half the post suddenly sounds like a math textbook.
Editing along the way also keeps you from realizing too late that you forgot the core analogy you wanted in the intro. Because if GPT misses it in Step 2, and you miss it in Step 3, by Step 6 it’s gone forever.