Refactoring AI-Generated Code: When to Keep vs Rewrite
I tested this.
When should you refactor AI code?
Refactor when: the code works but has inconsistent patterns, the architecture is sound but error handling is missing, or the naming conventions are wrong. These are fixable with automated tools and linters. Refactoring takes 30-60 minutes for most AI-generated features.
When should you rewrite it?
Rewrite when: the architecture is fundamentally wrong, the AI used the wrong approach for the problem, or the code has security issues. I have a 30-minute rule — if I cannot fix AI code in 30 minutes, I delete it and rewrite. This has saved me countless hours of debugging fundamentally broken code.
What is the 30-minute rule?
The 30-minute rule applies when: the AI makes the same mistake repeatedly, the code is over-engineered (unnecessary dependencies, complex patterns), or the logic is wrong at the design level. I used it on a WebSocket implementation that had a race condition. Two hours of debugging, deleted, 1 hour rewrite. The rewrite worked on first deploy.
Refactor when patterns are inconsistent. Rewrite when architecture is wrong or security is compromised. 30-minute rule: if you cannot fix in 30 minutes, rewrite from scratch.
The 30-minute rule is the most important AI development skill I learned. Developers waste hours debugging AI code because of sunk cost fallacy — they already invested time, so they keep investing. Delete the broken AI code and write it yourself. You will be faster and the result will be better. AI is great for first drafts. Humans are better for rewrites.
Give me steps and mistakes.
AI tools are powerful but require human judgment. Review every output before merging. Test every feature with edge cases. Never trust AI code in production without verification. These habits prevent production incidents. The extra minutes spent on review pay for themselves many times over in prevented issues and cleaner codebases that are easier to maintain long term.
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