Matthew Goddard

Prompt chaining (and why it matters more than you think)

I’ve been experimenting with prompt chaining because I needed to get something done that a single prompt just couldn’t handle.

I was trying to pull together a draft proposal from a bunch of messy notes. Usually, that’s fine, but the jump from “here are my notes” to “here’s a coherent, human-sounding doc I can share” is where most of the value (and the pain) lives.

Breaking that work into a few smaller steps, asking for a summary first, then pulling themes, then drafting each section one at a time, helps A LOT.

The final doc still needed a pass, obviously. But I wasn’t starting from a blank page and the thinking behind it felt more deliberate.

What actually is prompt chaining?

Prompt chaining is just passing the baton from one prompt to another. Instead of trying to get the AI to do a big job in one go, you break it into parts. Like:

– First: “Summarise these notes.”
– Then: “Group the points into themes.”
– Then: “Write an intro paragraph based on that.”
– And finally: “Make it less dry.”

You can string those steps together like a little assembly line. You can even run a few steps in parallel, then stitch the results back together at the end.

The great this you can do this in your preferred chat tool by copying and pasting from one prompt to the next, OR you can create a workflow in Copilot Studio, Zapier, Make.com
Where I’ve used it

A few places where this has helped:

* Discovery synthesis: Summarising interview notes, extracting user goals, and then mapping those to opportunity areas.
* Persona building: clustering behaviour patterns, naming them, then generating narratives from the clusters.
* Proposal work: rough first draft from a brief, then tone-of-voice adjustment, then layering in case studies.
* Workshop prep: turning challenges into HMWs, generating ideas, ranking them—all as separate turns.

It’s not magic. But it’s faster than doing it manually, and it still leaves room for judgment at the end.

### What to watch out for

There are some obvious traps.

If the first step is off, the rest usually is too. And if you’re chaining loads of prompts together without stopping to check, you can end up over-engineering something that would’ve been quicker to write yourself.

Sometimes it’s better to just write the damn thing.

Why it’s worth learning anyway

Most people trying to use AI in their work start with the question “How can I get it to do this entire task for me?” which is rarely the right question. A better one might be: “Where in this task do I slow down or get stuck?”

Then you can ask AI to help with that bit. Chain a few of those together, and you’ve got a process that actually works.

Not every job needs a chain. But some definitely do.