The Four Layers That Make a GPT Feel Genuinely Useful

You’ve probably got at least one Custom GPT that you keep going back to.

Maybe you found it in someone’s course, bought it as part of a toolkit, or picked it up from a recommendation in a Facebook group.

You didn’t create it, and you probably have no idea what instructions are behind it. You just know that when you need to do a particular job, that’s the GPT you open.

Now imagine trying to replace it.

You tell ordinary AI what the GPT does, give it a similar task, and get back something that technically meets your request.

Except it’s not nearly as useful.

What’s missing?

It may be something you haven’t even noticed about the way your favourite GPT works.

And if you’re planning to turn it into a Skill, you’ll want to identify those things before you start.

In a Nutshell

A useful Custom GPT has more going for it than the job it performs.

There are four layers worth examining:

1. Function: What does it do?
2. Workflow: How does it take you through the task?
3. Judgement: What does it notice, question or prioritise?
4. Distinctive Method: What makes you choose this GPT instead of ordinary AI?

You can examine all four through normal use, even when someone else created the GPT.

Understanding them gives you a much clearer picture of what your own Skill will need to preserve.

Layer 1: Function. What job does it do?

This is usually the easiest one to identify.

You might have a GPT that helps you:

• Write sales emails.
• Plan a new product.
• Analyse customer feedback.
• Turn long content into social posts.
• Make decisions about what to work on next.

Its function is the job you ask it to perform.

Let’s use a product-planning GPT as our example.

You give it a rough product idea, and it helps you develop that idea into something you could actually sell.

That’s its function.

Simple enough.

But if you tell ordinary AI, “Help me develop a product idea,” you’ll probably get a result that’s quite different from what your favourite GPT produces.

We need to look a little further.

Layer 2: Workflow. How does it take you through the task?

Think about what happens between opening the GPT and receiving your finished result.

Does it ask questions before it starts?

Does it work through the task in stages, checking with you before continuing?

Perhaps it asks you to choose between a few directions rather than producing everything at once.

Our product-planning GPT might have a particular sequence.

First, it asks who the product is for and what problem it solves. Then it helps you narrow the idea, considers what the customer actually needs, and only starts developing the product once those decisions have been made.

That’s a very different experience from an AI that immediately produces a complete product outline based on your first sentence.

And you may not have realised how much you appreciated that sequence until you tried working without it.

When you’re examining a favourite GPT, pay attention to how it gets you to the result, not just the result itself.

Layer 3: Judgement. What does it notice that ordinary AI might miss?

This one’s particularly interesting.

A useful GPT may make decisions throughout the process that you don’t consciously notice.

It might recognise when you’ve given it too little information, question an assumption, or notice that your request doesn’t quite match what you’re trying to accomplish.

Let’s go back to our product-planning example.

You tell the GPT you want to create a comprehensive course about a particular subject.

Instead of immediately helping you outline twelve modules, it asks how much time your intended customer has available and what they actually need to accomplish.

After hearing your answer, it suggests that a much smaller product might be worth considering.

You could disagree, of course. But the GPT has noticed something relevant and brought it to your attention.

That’s judgement.

Other examples might include a GPT that:

• Recognises when an idea is becoming unnecessarily complicated.
• Asks for missing information rather than inventing it.
• Questions whether a recommendation fits your available resources.
• Notices inconsistencies between different parts of a plan.
• Knows when to stop asking questions and get on with the work.

These behaviours can be easy to overlook because they happen along the way.

But they’re often important to the experience you want to preserve.

Layer 4: Distinctive Method. What makes this GPT worth choosing?

Here’s where we bring everything together.

You’ve identified the job, the workflow and some of the judgement the GPT demonstrates.

Now ask yourself:

Why do I choose this particular GPT instead of another tool that does something similar?

Maybe our product-planning GPT consistently helps you turn ambitious ideas into small, realistic products.

Perhaps it uses a particular framework that makes the decisions easier.

Or maybe it combines several useful behaviours in a way that suits how you work.

Its distinctive method may involve a combination of all the previous layers, plus something else you’ve noticed through using it.

You don’t necessarily need to know how the creator built that method.

You need to be able to describe the experience and the value it creates for you.

For example:

“I use this GPT because it helps me take a big product idea, work out what the customer actually needs, and develop something manageable without getting carried away adding features.”

That’s considerably more useful than saying:

“It helps me create products.”

And it gives you a much better starting point for building your own Skill.

Let’s put all four layers together

Here’s what our product-planning example might look like once we’ve examined it.

Layer	What we’ve noticed
Function	Helps develop product ideas.
Workflow	Asks questions, narrows the idea, then develops the product in stages.
Judgement	Notices when an idea is becoming too complicated or doesn’t match the customer’s needs.
Distinctive Method	Helps turn ambitious ideas into manageable products without losing the original value.

See how much more information we have now?

If we only preserve the function, we could end up with a generic product-planning assistant.

But when we understand the other three layers, we’ve got a much clearer picture of what made the original GPT worth using.

That information can guide the creation and testing of an independent Skill.

But what if someone else created the GPT?

That’s actually the main situation I’m thinking about here.

Many of us have collected useful GPTs created by other people. We may have received them through workshops, memberships, courses, purchases or shared recommendations.

We don’t necessarily have access to their original instructions, reference files or other behind-the-scenes material.

And we shouldn’t try to extract someone’s protected work.

Instead, we can examine what the GPT does through normal, permitted use.

You can observe the questions it asks, the way it responds, the choices it makes and the results it produces.

You can also ask it to explain its approach where appropriate, while recognising that its description may not perfectly reflect how it actually operates.

If the creator already offers an official Skill or another portable version, check that option first.

Otherwise, these four layers give you a way to start identifying the useful behaviour you’d like to recreate independently.

You’re building your own way of accomplishing the work, based on what you’ve legitimately learned from using the original.

And if you created the GPT yourself?

You can use exactly the same four-layer approach, with the added advantage of having access to your original instructions and supporting materials.

Don’t confuse a good description with a good replacement

There’s one more thing to remember.

You might create a beautiful description of all four layers and still end up with a Skill that doesn’t behave quite the way you expected.

That’s why testing matters.

Once you’ve created a Skill version, give it and the original GPT the same representative tasks.

Compare more than their final answers.

Did the new Skill ask useful questions? Did it follow an appropriate sequence? Did it notice the things you valued in the original?

And most importantly, does it still give you the kind of help that made you choose that GPT in the first place?

The answers don’t need to be identical. You’re checking whether the important value has survived.

Once you’re satisfied with that, you can start personalising the Skill to fit your business and preferences even better.

Try this with one of your favourite GPTs

Choose a GPT created by someone else that you regularly use.

Open it and give it a familiar task.

As you work through the interaction, make a few notes using these four questions:

Layer	Your notes
Function	What job do I use it for?
Workflow	How does it take me through that job?
Judgement	What does it notice, question, decide or prioritise?
Distinctive Method	Why do I choose this GPT over ordinary AI?

You don’t need lengthy answers. A sentence or two for each is enough to get started.

You may find that you can describe some layers immediately, while others take a little more observation.

That’s useful information too.

You’re beginning to identify what you’ll want to keep when you create your own version.

Your next step

In my GPTs to Skills workshop, we’ll work with the Custom GPTs you’ve already collected, including those created by other people.

We’ll look at how to identify their useful behaviour and carry that value forward into an independent Skill you can maintain, personalise and adapt.

The process also works with GPTs you’ve created yourself.

Bring one GPT you genuinely wouldn’t want to lose.

[Find out more about the GPTs to Skills workshop.]

I Have Questions

Do I need to understand all four layers before I can turn a GPT into a Skill?

No. You can start with what you’ve already noticed through using it. The conversion and testing process can help you identify important details you initially missed.

Can I use this approach with a GPT I purchased?

Yes, provided you work within the creator’s terms and use information legitimately available to you. Check whether the creator offers an official Skill or alternative first. The aim is to create your own independent implementation, not reproduce protected material.

What’s the difference between workflow and distinctive method?

Workflow describes the steps the GPT takes you through. Distinctive method describes the particular combination of approaches and behaviours that makes you choose it over other tools.

What if I can’t identify the GPT’s judgement?

Try giving it a familiar task with a missing detail or a conflicting requirement. Notice whether it asks questions, makes assumptions, identifies the conflict or changes its recommendation.

Will my new Skill produce exactly the same results?

Not necessarily. Different AI platforms and implementations may behave differently. The goal is to preserve the important function, workflow, judgement and distinctive value, then test how well your independent version performs.