The speed of going from zero to a working demo with AI is addictive. But the real headache starts when you have to refactor that code after two weeks of user feedback.

I realized that without strict naming rules good docs and clear testing modifying early AI code takes twice as long as writing it manually.

Now I force myself to slow down right after the initial build to clean up the logic before touching any new features.

Do you clean up your AI-generated code right after the prototype works, or do you wait until things start breaking?

Everyone talks about how fast AI lets you spin up a MVP but nobody warns you about iteration two.

Building from scratch takes a few hours going back weeks later to edit old code gets confusing fast. I've stopped treating the first generated version as a finished product and started looking at it as just a rough design draft.

At what point do you guys stop prompting for new features and start manually locking down the codebase structure?

We spent almost three weeks building a detailed six-step on boarding survey to collect user demographics before giving them access to the workspace. We thought collecting deep setup preferences upfront would help us personalize their dashboard experience. Looking back, nearly half of our trial signups dropped off right at step three without ever seeing the app. Once we bypassed the setup wizard and let users land directly on a sample project, our activation numbers jumped instantly.

Is upfront user profiling just an unnecessary barrier that kills early adoption?