The Lovable-to-Production Checklist: 12 Things to Verify Before Real Users Arrive
Lovable, Bolt, v0, and Replit are remarkable tools. They compress what used to be months of development into an afternoon, and for validating an idea, nothing beats them. But there is a difference between an app that works in a demo and an app that survives contact with real users — and that difference is invisible until it isn’t.
This checklist is the same one our engineers walk through when a founder brings us an AI-built app. You don’t need to be technical to use it. For each item, we explain what to check, why it matters, and how to ask your AI tool (or a developer) to verify it.
1. No secrets in the source code
API keys, database passwords, and payment credentials must never appear in code that ships to the browser. This is the single most common problem we find, and the most dangerous: anyone who opens their browser’s developer tools can read your frontend code, take your OpenAI or Stripe key, and run up charges on your account.
How to check: ask your AI tool, “Search the entire codebase for any hardcoded API keys, tokens, or passwords, including in frontend files. List every file where credentials appear.” Anything it finds in a file that isn’t a server-side environment configuration needs to move.
2. Server-side validation on every input
AI builders love form validation — in the browser. The problem is that the browser is the user’s territory, not yours. Anyone can bypass your form and send raw requests directly to your backend. If the server accepts whatever arrives, your database will happily store garbage, negative prices, or someone else’s user ID.
How to check: “For every API endpoint, show me where the incoming data is validated on the server before being written to the database.” If the answer involves only frontend form checks, that’s a gap.
3. Authorization, not just authentication
Authentication asks “who are you?” Authorization asks “are you allowed to do this?” AI-generated apps usually nail the first and skip the second. Login works, so everything looks fine — until someone changes the ID in a URL and reads another user’s data, or discovers the admin panel never actually checks who’s asking.
How to check: log in as a normal user, then try to access an admin URL directly. Try changing an ID in the address bar to view a record that isn’t yours. If either works, you have an authorization hole — and this is the category of bug that turns into a data breach.
4. Rate limiting on public endpoints
Without rate limiting, a single script can hit your signup form, your login endpoint, or your AI-powered feature thousands of times per minute. Best case, your app slows to a crawl. Worst case, your usage-billed API account (OpenAI, Twilio, SendGrid) absorbs the cost.
How to check: “Which endpoints have rate limiting applied? Show me the configuration.” Login, signup, password reset, and anything that calls a paid external API should all be on the list.
5. Error handling that fails gracefully
What happens when your database is briefly unreachable, or a third-party API times out? In most AI-built apps, the answer is a white screen, a cryptic error, or — worse — a half-completed operation that leaves data in an inconsistent state.
How to check: ask what the user sees when each external dependency fails. There should be a clear error message, no data corruption, and ideally a retry path. “The app crashes” is an answer you want to hear now, not in month two.
6. Database backups you have actually tested
Managed platforms like Supabase back up your data, but backups you’ve never restored are a hope, not a plan. Founders discover this the day an AI-assisted migration wipes a table.
How to check: confirm backups exist, know how far back they go, and — at least once — actually restore one to a test environment. If you can’t answer “how do I get yesterday’s data back?”, that’s the task.
7 through 12: the rest of the list
Seven: HTTPS everywhere, including redirects. Eight: dependency audit — AI builders pin whatever package versions they saw in training, some with known vulnerabilities; run an audit and update. Nine: logging and monitoring, because you can’t fix what you can’t see; at minimum, error tracking like Sentry. Ten: a staging environment, so changes get tried somewhere that isn’t production. Eleven: least-privilege database access — your app should not connect as a superuser. Twelve: a written record of what the app actually does, because the day you bring in a developer, the prompt history is not documentation.
None of these items is exotic. They’re the baseline that separates a prototype from a product. The good news: most of them are days of work, not months — if you know exactly which ones your app is missing.
Where to go from here
If you walked this list and found gaps, that’s normal — nearly every AI-built app we review has several. The question is prioritization: which gaps are urgent for your specific app, given your users, your data, and what the app handles.
Our free AI Prototype Risk Audit gives you a scored answer in five minutes, and our fixed-fee code audit turns it into a concrete plan a week later. Either way: run the checklist before your users run it for you.
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