Vibe Coding Best Practices: Avoid the Doom Loop with Planning and Code Reviews

I've written more software in the past six months than I've written in my entire lifetime. That's the power of vibe coding—using AI to transform natural language descriptions into working code.

What Is Vibe Coding?

Vibe coding is a way to create software by describing what you want in natural language and letting an AI write the code for you. Instead of writing code line by line, you describe your vision, the AI generates it, and you react to what you get.

The term was coined by AI researcher Andrej Karpathy in February 2025. He described a new way of working where you "say stuff, run stuff, and copy-paste stuff, and it mostly works." Since then, vibe coding has evolved from a novelty into a legitimate way to build production software.

The Vibe Coding Doom Loop

However, vibe coding isn't always smooth sailing. Most projects eventually hit a critical problem: the doom loop. This occurs when:

• You weren't clear about what you wanted
• Requirements changed multiple times and code reflects those twists
• The AI switched technologies mid-stream
• The three software layers (data, controller, view) fall out of sync

When these layers are out of sync, you end up with intractable bugs that the AI struggles to fix, no matter how many times you ask it to try.

Two Cycles to Avoid the Doom Loop

1. Plan-Review-Fix Cycle

The clearer you are about what you want, the better output you'll get from the AI. Instead of iterating in code, iterate on your plan first—in markdown. Work with an AI to think through your requirements, then have another AI review the plan for gaps, logical flaws, and blind spots.

This cycle ensures you know exactly what you want before you start coding, significantly reducing the likelihood of introducing tech debt and bugs.

2. Implement-Review-Fix Cycle

Agents make mistakes. That's guaranteed. When the coding agent completes its work, have a code-reviewer AI examine it for errors, duplicate code, security gaps, missing error handling, and insufficient test coverage. The reviewer doesn't fix issues—it reports them so you can decide what matters.

Three areas deserve extra attention:

• Error handling: What happens when APIs fail or edge cases occur?
• Test coverage: Are there comprehensive unit and integration tests?
• Security: Are there dependency vulnerabilities, exposed secrets, or inadequate input validation?

Debugging When Things Go Wrong

When you encounter a bug that the coding agent struggles to fix, don't let it keep trying. Instead, start a fresh conversation and ask a new agent to diagnose—not fix. This is crucial. You want to separate diagnosis from implementation to avoid introducing more bugs.

If it's a particularly tricky bug, spin up two or three agents with the same prompt and see if they converge on a diagnosis. Only when you're confident the real issue has been identified should you allow any code changes.

Key Takeaways

• Plan first, code second—work through requirements in markdown before you start building
• Always have code reviewed before you test it
• Separate diagnosis from fixes when debugging
• Keep conversations fresh to avoid context rot
• Question the AI's suggestions and ask for clarification
• Remember: AI agents are tools, not replacements for critical thinking

By following these cycles and best practices, you can transform vibe coding from a chaotic prototyping tool into a reliable way to build production software. The planning may feel like extra work upfront, but it saves countless hours debugging later.