Why AI-Generated Code Needs Human Review

AI coding assistants like Cursor, GitHub Copilot, and ChatGPT have revolutionized how we write code. They're fast, they understand context, and they can scaffold entire applications in minutes. But there's a catch: they consistently make the same types of mistakes.

The Problem with "Vibe Coding"

When you're in the flow, prompting your AI assistant and watching code appear like magic, it's easy to skip the review step. We call this "vibe coding" — trusting the AI output and moving on to the next feature.

But AI assistants are trained on code from the internet, which includes: - Tutorials that prioritize simplicity over security - Stack Overflow answers optimized for upvotes, not production use - Outdated patterns from years past

Common Issues We See

1. Hardcoded Secrets AI assistants often generate examples with placeholder API keys like `sk_test_xxx` or environment variables read directly in client-side code. Without proper review, these patterns slip through.

2. Missing Input Validation When scaffolding forms or API routes, AI tends to trust user input implicitly. You get working code, but not secure code.

3. Debug Code in Production Console.logs, TODO comments, and test routes are standard in AI-generated code. They're helpful during development but dangerous in production.

The Solution

This is why we built ProdReady — a pre-launch checklist for AI-generated codebases. In seconds, it scans your repository for the specific issues that AI coding creates.

Think of it as the experienced developer review that every AI-generated codebase needs, but automated and instant.

Key Takeaways

• •. **AI is a great first draft** — but not a final one
• •. **Security issues are predictable** — which means they're catchable
• •. **Automated scanning saves time** — and prevents costly mistakes

Ready to see what's hiding in your code? Scan your repository now.