The demo works. What about the product?

The best moment of building something with AI is seeing it work for the first time. You write a few sentences, make some adjustments, and something resembling the idea in your head appears on screen. What was just a conversation now has an address, buttons, perhaps a database.

At that moment, the same thought comes to mind: There it is. Done.

Then you refresh the page.

What the first screen doesn't tell you

A prototype usually shows the best possible case. The data arrives, the connection holds, the user clicks the right button. Everyone plays their assigned part.

The point where it starts becoming a product is a little less pleasant. What do we show when the result is empty? What happens if the same action runs twice? If someone leaves halfway through and returns tomorrow, can they pick up where they left off?

These things don't show up well in a screenshot. But they're how a user gets to know the application. They see the animation I'm excited about once; they remember their lost data for a long time.

As we get faster, there's more to check

AI can make a change very quickly. That speed doesn't automatically reduce the time I need to understand what it did.

Sometimes a single request changes a pile of files. The screen looks fixed. But if I move on to the next request without understanding the conditions under which the fix works, a few rounds later I can feel like a stranger in my own product.

So the word “done” needs a small question beside it: Based on what?

Did the build pass? Was the action actually saved? Does it work in the deployed version too? Has it been tried on a phone? Each answer tells me something different.

Following one small path all the way through

Here's a goal that makes more sense to me: choose one small task a user needs to do, and complete that path from start to finish.

If they're creating a record, they should be able to create it, open it again, and edit it. If something goes wrong, they should understand what happened. And I should have a trace that helps me find the error.

Then I can add the second task.

This approach might look less impressive on day one. But opening the project the next day and knowing where I stand feels good too. I don't have to manufacture fresh excitement every time; I can keep building on something that works.

If it still works tomorrow

AI has lowered the cost of trying an idea. I love the excitement of that. Not having to wait weeks to see something I've imagined is a huge opportunity.

Still, there's work to do after the first working version. Reading, trying things, simplifying, sometimes undoing a change. Going back to the place where a user got stuck.

The prototype shows me the idea is possible. For it to become a product, I need to stay with it a little longer.