Creating apps with AI: what actually happens when AI writes the code for you

What "building an app with AI" means

People use the phrase for a lot of different things, so let's pin it down. Here it means something specific: you write, in your own words, what you want the app to do, and an artificial intelligence writes the code that makes it work. You never see that code and you don't need to understand it. What you see is the app, ready to tap and use.

The process always follows the same loop, and knowing it up front saves you a lot of frustration. It isn't an order you place once and receive. It's closer to working with someone who builds very fast but needs you to tell them what's right and what isn't.

1. You describe You explain what job the app does, who uses it and what information it needs to keep. The more specific, the better.
2. The AI writes the code It turns your description into screens, buttons and logic. The first version is ready to try in minutes.
3. You test You open the app and use it the way the real user would. This is where you find what's missing or what got misunderstood.
4. You refine by chat You ask for each change in plain words and the AI applies it. You repeat the loop until the app does what you need.

The step most people skip is the third one. Testing isn't glancing at the screen for a second. It's entering a real piece of data, closing the app, opening it again and checking that it's still there. It's picturing the person who will use it in a hurry, on their phone, without reading any instructions.

What AI is good at, and where it struggles

AI is very good at the things that show up again and again in similar apps: lists, forms, search boxes, counters, simple calendars, screens that show and sort information. If what you want looks like something that already exists in many forms, the result usually comes out right from the start.

Where it struggles is guessing what you didn't say. It doesn't know your business, how many people work for you or what time you close. If your request is vague, it fills the gaps with reasonable assumptions, but they're still assumptions. AI rarely gets it wrong for lack of skill; it gets it wrong for lack of context.

AI rarely gets it wrong for lack of skill; it gets it wrong for lack of context.

• Be specific: say who uses the app, what job it does and what each person needs to see.
• Check the result: don't assume something works until you've tried it with real data.
• Improve step by step: one change per message is easier to get right than ten changes at once.
• If something doesn't come out right, rephrase: describe the problem in other words or with a concrete example.

Why context changes the result

Because the AI fills in everything you leave out with guesses, the same idea can turn into very different apps depending on how much context you give. Look at two versions. With a vague request like "I want an app for my shop," the AI has to decide on its own what you sell, what gets recorded and who uses it. With a request that carries context, for example: "I want an app to keep track of orders customers place by phone. Each order has the customer's name, the product, the quantity and whether it's been delivered. Show me the pending ones first."

With the vague request, almost everything you see would be the AI's invention. With the one that carries context, it already knows which fields to keep, which statuses exist and what goes at the top. Then come the adjustments, also by chat. For example: "Add a delivery date and sort the pending orders by it." Or: "Add a button to mark an order as delivered without opening it."

Juan Belmont, the founder of Miniceo, builds and tests his own apps every day, and he puts the lesson this way: the best requests describe one concrete task and who uses the app. Start small, then improve by chatting. One of those apps is a golf game for playing against other people. Its first version had beeps generated by code instead of real sounds; he asked for real sounds, and since then real sounds are the norm in apps built this way.

AI vs. templates: the difference that matters

Before AI, making an app without coding almost always meant picking a template. A template is an app already built for a typical use: you choose the one closest to yours, change the colors, text and photos, and adapt to whatever it comes with. If the way you work doesn't fit the template, you're the one who changes, not the app.

With AI, the starting point is flipped. You don't choose from a fixed menu: you describe how you work and the app is built from that. If you need an unusual field, a status that only exists in your business or a screen nobody else would use, you ask for it. The trade-off is that there's no predictable result ahead of time: each app comes from your description, so the quality of that description matters much more.

A simple way to decide: if what you need is standard and you're happy with something identical to what everyone else uses, a template may be enough. If the way you do things has its own details, describing it and letting the AI build it usually fits better.

What it doesn't do yet

So you know what to expect before you start, here are the current limits, stated plainly:

• Your app isn't published under your own name in the stores where people download apps: people open it from your link.
• The app doesn't take payments: there's no card checkout or purchase flow inside it.
• It doesn't connect to the systems you already use, like your point-of-sale, accounting or booking software.
• A first version is simple; a more complex app takes several rounds of changes by chat, and some requests need to be rephrased.
• Everyone who uses the app sees the same saved data: it isn't a system of private client accounts.