From Vibe Coding to Production: Why AI-Generated Code Needs Expert Review

The Vibe Coding Revolution

Tools like Cursor, GitHub Copilot, and Claude have changed what's possible. A founder with an idea can now describe features in plain English and get working code in minutes. It feels like magic.

And for prototyping, it is magical. But there's a dangerous gap between "it works in demos" and "it works in production."

What AI-Generated Code Gets Wrong

1. Security Vulnerabilities

AI models trained on public code learn both good and bad patterns. Common issues we find:

• SQL injection vulnerabilities
• Cross-site scripting (XSS) exposure
• Insecure authentication implementations
• Exposed API keys and secrets
• Missing input validation
• Improper access controls

2. Error Handling (or Lack Thereof)

AI code often handles the happy path beautifully while ignoring edge cases:

• What happens when the database is unavailable?
• What if the user submits unexpected input?
• How does it handle network timeouts?
• What about concurrent users modifying the same data?

3. Scalability Issues

Code that works for 10 users can fail spectacularly at 1,000:

• N+1 database queries that multiply with scale
• Missing database indexes
• No caching strategy
• Synchronous operations that should be async
• Memory leaks from improper resource cleanup

4. Architecture Problems

AI generates code snippet by snippet, missing the big picture:

• Inconsistent patterns across the codebase
• Tightly coupled components that resist change
• No clear separation of concerns
• Database schema that doesn't scale

Real-World Horror Stories

The Right Way to Use AI Coding Tools

1. Prototype aggressively: Use AI for rapid validation of ideas. This is where it shines.
2. Treat output as draft: AI code needs review and refinement, not blind deployment.
3. Understand what it generates: If you can't explain the code, you can't debug it.
4. Get expert review before launch: A few hours of professional review prevents weeks of problems.
5. Plan for production: Use AI output as a starting point, then engineer for production.

Conclusion

AI coding tools are incredible for accelerating development and enabling non-technical founders to validate ideas. But production applications require expertise that AI cannot yet provide: security awareness, scalability thinking, and architectural wisdom.

The smart approach: use AI to move fast, then bring in experts to make it production-ready. The cost of a code review is tiny compared to the cost of a security breach or a failed launch.