Most people use AI like a search engine. You ask it something, it gives you an answer, and you move on. It works… to a point. But there’s a layer of thinking most people miss – one that separates adequate AI use from genuinely effective AI use. And it’s not about better prompts or fancier tools. It’s about understanding the layers of thinking that sit beneath every interaction with AI.

I’ve been thinking about this a lot lately, partly through my own learning, but mostly through conversations with people who are already using AI. They’re using it, sure, but they’re frustrated. The outputs feel generic. The time savings aren’t materializing. They’re starting to wonder if AI is actually worth the hype, or if they’re just not smart enough to use it properly. Spoiler: it’s neither. The problem isn’t them. It’s that they’re treating AI like a tool that solves problems directly, when what it’s actually good at is helping you think through how to solve problems. That’s a fundamental difference, and it changes everything.

Why People Start Here (And Why It Feels Sufficient)

Surface-level AI use is intuitive. You have a problem, you ask AI to solve it, and sometimes it does. Or at least it gives you something to work with. It feels efficient. It feels like you’re getting value. And in a narrow sense, you are.

The reason this works at all is because AI is genuinely capable. You can ask it to summarize a document, generate ideas, write an email, or explain a concept, and it’ll do a reasonable job. So people stop there. They’ve found a use case that works, and they repeat it. Why would they think there’s more?

The issue is that this approach treats AI as a lookup tool – a very sophisticated search engine. And when you use it that way, you get search engine results: surface-level, generic, shaped by whatever biases or assumptions you brought to the question in the first place.

What You’re Actually Missing

Here’s where it gets interesting. The people I talk to who are getting real value from AI aren’t using it differently because they’re smarter or because they found better prompts. They’re using it differently because they’re thinking about the problem differently.

Instead of asking AI to solve something, they’re asking it to help them design how to solve it. They’re thinking about the workflow, the context, the constraints, the desired outcome. They’re using AI as a thinking partner, not a task executor.

This requires more upfront thinking. It’s not faster in the moment. But the outputs are exponentially better, and more importantly, they’re yours – shaped by your thinking, your constraints, your reality.

The Onion Model: Understanding the Layers

I’ve been working through a framework that helps explain this. Think of effective AI use like an onion. There are layers, and each layer wraps around the one beneath it. You can’t skip layers and expect the result to work.

The Core: The Problem You’re Actually Solving

At the heart of everything is the problem itself. Not the task. The problem. There’s a difference.

If you’re asked to produce documentation, the task is “write documentation.” But the problem might be “our team doesn’t understand how this process works, and it’s causing delays and errors.” Or it might be “we need to onboard new people faster without burning out our experienced staff.” Same task, completely different problems.

Most people skip this step. They jump straight to the task and ask AI to execute it. But if you haven’t clarified the actual problem, AI can’t help you think through the best way to solve it. It’ll just generate documentation. Generic, probably fine, but not shaped by what you actually need.

This is where the thinking starts. And it’s harder than it sounds, because you have to sit with the problem long enough to understand it properly. You have to ask yourself: What’s really going on here? What are we trying to achieve? What’s getting in the way?

The Workflow Layer: How You’ll Solve It

Once you’ve got the problem clear, the next layer is the workflow – the process or sequence of steps you’ll use to solve it.

This is where AI becomes genuinely useful. Because designing a workflow is a thinking problem, not an execution problem. You need to consider: What steps are involved? In what order? What information do you need at each stage? What decisions need to be made? What constraints are we working within?

You could sit alone and think through this. Or you could use AI as a thinking partner. You could ask it: “Given this problem and these constraints, what would be an effective workflow for solving it?” And then you iterate. You push back. You refine. You’re not asking it to do the thinking for you; you’re asking it to help you think more clearly.

This is where the real value starts to emerge. Because a well-designed workflow, informed by your constraints and your reality, is something you can actually execute. It’s not generic. It’s yours.

The Context Layer: Preparing AI to Help

Now you’ve got a problem and a workflow. But AI doesn’t know your context. It doesn’t know your constraints, your environment, your audience, your limitations. It doesn’t know what success looks like for you.

So the next layer is context. This is where you tell AI: “Here’s what we’re working with. Here’s what we’re trying to achieve. Here’s what matters to us. Here’s what we’re constrained by.”

The more specific you can be, the better AI can help. Not because it makes the prompts fancier, but because it allows AI to make better decisions about what matters and what doesn’t. It’s the difference between asking AI to “write documentation” and asking it to “write documentation for a team of 12 people with mixed technical backgrounds, who need to understand this process in 15 minutes, and who will be using it as a reference guide while they work.”

The first prompt is vague. The second one is rich with context. And the outputs will reflect that difference.

The Prompt Design Layer: Asking the Right Question

By this point, you’ve done the thinking. You know the problem. You’ve designed the workflow. You’ve gathered the context. Now you need to ask AI to help you execute, and you need to ask in a way that reflects all of that thinking.

This is where prompt design comes in. But it’s not about being clever or using special formatting tricks. It’s about being comprehensive. It’s about translating your thinking into a question that AI can actually work with.

A good prompt at this stage isn’t a one-liner. It’s a brief that includes: the problem, the workflow you want to follow, the context, the constraints, and what success looks like. It’s specific. It’s grounded. It reflects the thinking you’ve already done.

When you do this, AI stops generating generic outputs. It starts generating outputs that are shaped by your thinking, your constraints, your reality.

The Meta Layer: Using AI to Help You Think

Here’s where it gets recursive. Once you understand that AI is good at helping you think, you can use it to help you think about how to use it.

You can ask AI: “I’m trying to solve this problem. What would be an effective workflow for approaching it?” You can ask it to help you identify what context matters. You can ask it to help you design a prompt that will get you the output you need.

This is the sophistication layer. This is where people who are getting real value from AI tend to operate. They’re not just using AI to execute tasks. They’re using AI to help them think through how to use AI effectively.

It sounds meta because it is. But it’s also where the compounding value starts to show up.

Why This Matters Now

We’re at a point where AI is becoming embedded in how work gets done. It’s not a novelty anymore. It’s a tool that people are expected to use, and use well.

The people who’ll thrive with AI aren’t the ones who found the best prompts or the fanciest tools. They’re the ones who learned to think about problems differently. They’re the ones who understand that AI is a thinking partner, not a task executor. They’re the ones who’ve learned to layer their thinking.

And here’s the thing: this isn’t complicated. It’s not rocket science. It just requires a different approach than the one most people default to.

It requires pausing before you ask AI anything. It requires asking yourself: What’s the actual problem here? How would I design a workflow to solve it? What context does AI need to understand? What does success look like?

Those questions take time. But they transform what AI can do for you.

What’s Next

Understanding the model is one thing. Applying it is another. In the next piece, we’ll walk through how to actually use this framework. We’ll work through a real example. We’ll show you what this looks like in practice, and how to build it into your workflow.

But for now, the invitation is simple: next time you sit down to use AI, pause. Don’t ask it to solve the problem. Ask it to help you think through how to solve it. Ask it to help you design the workflow. Give it the context it needs. Then ask it to help you execute.

That’s where the real sophistication lies. That’s where you’ll find the value that most people are missing.