Fixing AI-Generated Code: A Production Checklist

AI tools like Lovable, Bolt, Cursor, and v0 are genuinely magical for going from idea to working prototype in an afternoon. But there's a wide, dangerous gap between "it works on my screen" and "it's safe to put in front of paying customers." This is the exact checklist we use when we productionize an AI-generated app.

Why AI-generated code needs hardening

AI coding tools optimize for making the demo work, not for security, scale, or maintainability. The result usually looks great and hides serious gaps: no real authentication, secrets in the client, no database migrations, no tests, and business logic scattered wherever the model happened to put it. None of that matters until you have real users — and then all of it matters at once.

The production checklist

1. Authentication and authorization

AI prototypes frequently fake auth or leave it wide open. Before launch:

• Replace mock auth with a real provider or a properly implemented session system.
• Enforce authorization on the server, not just by hiding buttons in the UI.
• Verify that user A can never read or mutate user B's data.

2. Secrets and API keys

• Move every API key out of client-side code into server environment variables.
• Rotate any key that was ever committed or shipped to the browser.
• Put third-party calls behind your own API routes so keys never reach the client.

3. The data layer

This is where AI-generated apps are weakest.

• Define a real schema with proper types, constraints, and relationships.
• Add migrations so schema changes are versioned and reversible.
• Add indexes for the queries you actually run.
• Validate all input server-side (never trust the client).

4. Input validation and error handling

• Validate every input with a schema (we use Zod) on the server.
• Handle failure paths — network errors, empty states, and rate limits — gracefully.
• Never leak stack traces or internal details to users.

5. Tests

• Add tests for the critical paths: auth, payments, and anything that touches money or data.
• Set up CI so tests run on every change.

6. Performance and scale

• Fix N+1 queries and unbounded loops the model may have written.
• Add caching where it's safe.
• Load-test the paths you expect to be hot.

7. Security basics

• Rate-limit public endpoints and forms.
• Add CSRF protection where relevant and sanitize any rendered user content.
• Review dependencies for known vulnerabilities.

8. Observability

• Add error tracking and basic logging so you find problems before your users report them.
• Instrument analytics so product decisions are based on real usage.

What to keep

Here's the good news: you usually don't throw the prototype away. The UI and the product thinking are often genuinely good — that's what the AI tools are best at. We keep the interface your users will love and rebuild the risky foundations underneath it.

How long does it take?

For a typical AI-generated MVP, hardening to production takes two to four weeks — dramatically faster than starting from scratch, because the product direction is already validated.

The bottom line

AI prototyping tools are a superpower for getting to a working idea fast. They are not a substitute for the engineering that makes software safe to run. If you have a promising AI-built app and need it production-ready, we do exactly this — and you'll keep the UI you already like.