Why Your AI-Built App Breaks in Production

AI coding tools are probably the most impressive thing happening in tech right now. You describe what you want, it builds a genuinely good looking app, and the whole thing just... works. In the preview.

Then you try to put it in front of real users and everything falls apart.

I'm not picking on any specific tool here. This happens with every platform that abstracts away complexity. The abstraction works great until it doesn't. And production is where "doesn't" lives.

Why Does the Preview Environment Lie About Production Readiness?

Because preview runs your app in a sandboxed world where everything is configured perfectly, and production is none of those things. The database is right there, auth is pre-wired, network conditions are ideal, and the connection uses privileged keys. It's a beautiful illusion.

Your real users are on slow phones with spotty connections. Your database has actual data in it, not clean seed data. Your auth tokens expire at the worst possible moment. Your API has rate limits you never hit during development. Your CDN sometimes serves stale assets that break your latest deploy. None of this shows up in preview. All of it shows up the moment a real person tries to use your app. And by then you're scrambling to figure out what went wrong while your users bounce. The gap between preview and production is the gap between a demo and a product.

What Are the Most Common Production Bugs in AI-Built Apps?

The biggest offenders are database access policies, auth redirects, performance issues, and mobile rendering. These four account for most of the "it worked in preview" moments I see.

Row Level Security on your database. AI tools set up tables but don't always configure access policies correctly. Preview works because the connection uses a privileged key. In production, your users hit a restricted key and can't read their own data. The fix is adding the right policies, but that requires understanding your data model, the part the AI abstracted away.

Auth Redirect URLs. The redirects are set for localhost or the preview domain. Deploy to your own domain and users click "Sign In" only to land on a page that doesn't exist. Go into your auth provider's dashboard and add your production domain. Five minutes if you know where to look, three hours if you don't.

Why Do AI-Built Apps Feel Slow After Deployment?

AI tools tend to generate pages that fetch data at render time, which works in preview because the server is fast and close. In production, fetching on every page load makes your app feel sluggish. And if the API goes down, your whole app goes down with it.

Consider whether some of your pages could be statically generated or cached. It depends on your use case, but it's worth thinking about before users start complaining. On top of that, mobile responsiveness is another silent killer. AI tools generate responsive layouts, but "responsive" in preview and "responsive" on an actual phone are different conversations entirely. Test on real devices before you ship. Or at least use Chrome DevTools with actual device dimensions, not just the generic "responsive" mode. These performance and rendering issues are fixable, but you have to catch them first.

Do You Need Developer Skills to Ship a Vibe-Coded App?

Yes, at least a little. The gap between "AI built it" and "it works in production" still requires some developer knowledge to cross. Not always a lot. Sometimes it's 10 minutes of work. But you need to know which 10 minutes.

If you're a vibe coder stuck at this stage, you have two options. Learn the specific thing that's broken, which is totally doable but time consuming. Or get someone who already knows to fix it for you. That second option is what VibeFix exists for. Post your production bug as a bounty. Tell us what's broken, what you expected, what actually happens. A developer will pick it up and fix it. Your app is 95% there. Don't let the last 5% stop you from shipping. Post a bounty and get it across the finish line.