We’ve covered a lot of ground in this series. In Part 1, we looked at why “vibe coding” — describing what you want and hoping the AI figures it out — usually fails. In Part 2, we talked about why breaking your problem into small, specific pieces is the key to getting good results.

Now it’s time to actually build something.

Here’s the framework I use every time I sit down to work on a new project with AI. It’s simple, it works, and once it clicks, you’ll wonder how you ever worked any other way.

Step 1: Start With the Data, Not the Features

Before you write a single line of real code, figure out what your app needs to keep track of.

Every app is really just a set of data that gets displayed, updated, or acted on. A to-do app keeps track of tasks. A game keeps track of the player’s score, position, and health. A store keeps track of products and orders.

Make a list of the key “things” your app works with. Then ask the AI to help you define the structure of those things — what information they contain, and how they relate to each other.

This is like laying the foundation before you build the walls. Get this right, and everything else becomes easier.

Example prompt: “I’m building a simple task manager. Each task has a title, a due date, and a status (done or not done). Can you create a TypeScript interface for a Task?”

Notice how specific that is. No guessing required.

Step 2: Build the Simplest Thing First

Now that you have your data structures, what’s the single most basic action a user can take?

Not the whole app. Not the login screen, the dashboard, and the settings page. Just one thing. The most essential action.

For a task manager, that might be: add a new task to the list.

Ask the AI to build just that. One function. One component. Test it. Make sure it works. Only then move on to the next piece.

This is the part most people skip because it feels too slow. But it’s actually the fastest way to build something solid. You catch problems early, when they’re easy to fix, rather than late, when they’re tangled up in everything else.

Example prompt: “Using the Task interface we just created, write a function called addTask that takes a title and due date as arguments and adds a new task to an array.”

Step 3: Test, Then Take the Next Small Step

Once your first piece works, look at what logically comes next. What feature builds directly on what you just built?

In our task manager example: now that you can add tasks, you probably want to display them. So next, ask the AI to help you build a simple list component that shows all tasks.

Then test that. Then move on.

Each step should feel like a small, natural extension of the last one. If a step feels too big, break it down further.

Why This Works So Well

When you work this way, the AI is never guessing. It always has exactly the context it needs — because you’ve built up that context piece by piece, and you’re feeding it back in with each new prompt.

You stay in control. You always know what’s working and what isn’t. And you can hand off specific, well-defined tasks to the AI while you keep your focus on the bigger picture.

It’s not magic. It’s just good engineering — with an incredibly fast assistant by your side.

Give this framework a try on your next project. Start small, be specific, and build one brick at a time. You might be surprised how much you can get done.