AI coding tools like Lovable, Cursor, Bolt, v0 and Replit are genuinely good at getting a working app in front of you fast. The problem is not the AI. The problem is shipping what it produces without hardening it, because roughly 45% of AI-generated code has issues that fail professional review: missing security controls, no tests, and a tangled structure that nobody can safely change. Making AI code production-ready means keeping the speed you gained and adding the boring, essential layers the demo skipped.
Why does AI-built code break in production?
A demo and a production system are judged on different things. A demo has to work once, for you, on your machine, with data you control. Production has to work every time, for strangers, on inputs you did not anticipate, while people try to abuse it.
AI tools optimise for the first case. They produce something that looks finished and runs on the happy path. That is a real achievement, and it saves weeks. But the AI has no idea what your actual users will do, what a hostile visitor will try, or how the code will need to grow over the next year. It fills those gaps with plausible-looking guesses.
So the app that impressed you on Tuesday starts throwing errors when a real customer signs up on Friday. Nothing went wrong, exactly. The code was never asked to handle Friday.
The short answer
- AI code usually works on the happy path and quietly ignores the rest.
- Security, data handling and error cases are the parts most often missing.
- Around 45% of AI-generated code would not pass a professional review.
- The fix is a hardening pass, not a rewrite.
What is actually missing?
When we review vibe-coded prototypes, the same gaps come up again and again.
Security. Secrets committed into the repository. API keys shipped to the browser. Database rules left wide open, so any visitor can read every user's records. Authentication that checks who you are but never checks what you are allowed to see. These are not exotic bugs. They are defaults the AI never closed.
No tests. The app has zero automated checks. That means every future change is a gamble. You fix one thing and break two others, and nobody notices until a customer does. Without tests, the code becomes frightening to touch, which is how promising projects grind to a halt.
Tangled structure. Business logic scattered across files, copied and pasted with small variations, no clear boundaries. It runs, but no human can hold it in their head. Adding a feature means guessing which of five similar functions is the real one.
Error handling. The happy path is covered. The unhappy paths, a failed payment, a dropped connection, a malformed upload, are not. In production, the unhappy paths are most of real life.
Data and privacy. Personal data logged in plain text, no consideration for retention or deletion, no thought given to GDPR. Fine in a demo. A liability the day you take a real user.
How do you take a prototype to production?
You do not throw the prototype away. That would waste the real value the AI gave you. You harden it deliberately, in a sensible order. Here is the checklist we work through.
1. Lock down secrets and access
Move every key and password out of the code and into environment variables. Rotate anything that has already been committed, because it must be treated as leaked. Tighten your database rules so users can only reach their own data. This is the first stop because it is the cheapest disaster to prevent and the most expensive to ignore.
2. Fix authentication and authorisation
Confirm that logging in and checking permissions are two separate things, and that both are enforced on the server, not just hidden in the interface. A button the user cannot see is not a security control. The server has to say no.
3. Add tests around what matters
You do not need to test everything on day one. Start with the paths that would hurt most if they broke: sign-up, login, payment, the core action your product exists to perform. Enough coverage that you can change code and know within seconds whether you broke something.
4. Handle the unhappy paths
Walk through what happens when things fail. A declined card, a timeout, an empty result, a file that is too large. Each one should produce a clear message and leave the system in a sane state, rather than a blank screen or a half-finished transaction.
5. Validate every input
Never trust data coming from the browser, even from your own forms. Check it on the server before it touches your database. This closes a whole category of security holes at once.
6. Untangle the worst of the structure
You do not need a rewrite. You need the riskiest, most-copied pieces pulled into one clear place, so future changes happen in one spot instead of five. Small, targeted cleanup, not a grand refactor.
7. Add logging and monitoring
When something breaks in production, and it will, you want to find out before your customers tell you. Basic error tracking and sensible logs, with personal data kept out of them, turn a two-day mystery into a ten-minute fix.
8. Get a second pair of eyes
Before you rely on the app, have an experienced engineer read it. A short code audit finds the gaps you cannot see yourself, and tells you honestly how far the prototype is from production. If you are raising money or being acquired, technical due diligence does the same with an investor's questions in mind.
Do you need to start over?
Almost never. The instinct to scrap everything and rebuild is usually a mistake. The prototype captured your product decisions, your screens, your flow. All of that is worth keeping. What it lacks is the engineering underneath, and that can be added to the code you already have.
This is the heart of our vibe-code cleanup work: take the app the AI built, keep the good parts, and harden the rest until it is safe to run and pleasant to extend. When a project has drifted further, into something broken or half-finished, fixing a broken MVP or a full software project rescue covers the same ground at a larger scale.
Key takeaways
- AI tools are a real accelerator. The risk is shipping the output untouched.
- The common gaps are security, tests, error handling, structure and data privacy.
- A hardening pass keeps your speed and adds the safety a demo skipped.
- You rarely need to start over. You need to finish the job the AI started.
A calm next step
If you have an app that works in the demo and you are unsure whether it is ready for real users, we can tell you. We are senior EU-timezone engineers, based in Moldova, GDPR-aware, and typically around 40% below Western rates. We have shipped live products including GazetAI, CarVinVin and Bank-Lift, and you always keep all your code.
Start with a free 15-minute triage call. Tell us what you built and what worries you, and we will give you an honest read on what it needs. See how we work, browse the portfolio, or get in touch when you are ready.