AI Coding
Vibe coding code review and refactor
A practical vibe coding code review workflow for finding bugs, security risks, data flow issues, missing tests, accessibility gaps, and refactoring safely after AI-generated changes.
AI-generated code can pass the demo path while still hiding broken assumptions. A good vibe coding code review looks for behavior risk before style preferences: bugs, security, data flow, tests, accessibility, performance, and maintainability.
This guide shows how to review and refactor safely after a vibe coding session. The goal is not to reject AI-generated code. The goal is to turn it into code you can explain, test, maintain, and change without fear.
Who this guide is for
- Developers reviewing AI-generated pull requests
- Founders preparing a vibe coded MVP for real users
- Product teams converting AI prototypes into maintainable features
- Designers and operators who need a review prompt before handing code to engineers
- Teams that want to refactor safely without breaking working behavior
Step-by-step workflow
- Review the user behavior first: what changed, what should work, and what could break?
- Ask the AI to list review findings by severity before making edits.
- Check for bugs, security, data flow, tests, accessibility, performance, and maintainability.
- Confirm auth boundaries, secret handling, database writes, API validation, and destructive actions.
- Identify missing tests or manual verification paths.
- Fix blockers before style cleanup.
- Refactor safely only after behavior is protected by tests or a precise manual check.
- Keep refactors narrow: rename, extract, deduplicate, or move logic without changing behavior.
- Run verification after each refactor group.
- End with a short review summary: findings fixed, tests run, residual risks.
Recommended tools
Code review and refactor prompt template
Use this prompt after an AI coding session:
Review the current change as a senior engineer. Focus on bugs, security, data flow, tests, accessibility, performance, and maintainability. List review findings first, ordered by severity, with file references where possible. Do not refactor yet.
After findings are clear, propose the smallest fixes. Only refactor safely after the behavior is protected by tests or a precise manual verification path. Avoid unrelated style changes, dependency changes, environment variable changes, and broad rewrites.
Refactor prompt template
Use this when behavior is already verified:
Refactor safely without changing behavior. The goal is [specific cleanup]. Preserve public routes, API contracts, tests, styles, and existing user workflows. Keep the diff small. Explain why each file changes and run the verification command after the refactor.
Review and refactor checklist
- Did the review check bugs, security, data flow, tests, accessibility, performance?
- Are findings ordered by severity instead of preference?
- Are auth, secrets, API validation, and database writes reviewed?
- Is each refactor tied to a concrete maintainability problem?
- Are behavior changes separated from cleanup changes?
- Did verification run after fixes and after refactors?
- Is the final diff small enough for a human to review?
Common mistakes
- Asking the AI to "clean up the code" before review findings are clear
- Mixing bug fixes and refactors in one large diff
- Treating generated code as safe because it compiles
- Reviewing only UI appearance and ignoring data flow
- Skipping accessibility and mobile behavior because the desktop demo works
- Letting the AI add dependencies for problems the project already solves
- Accepting broad rewrites without a stronger test suite
Practical example
Weak prompt: refactor this AI-generated dashboard.
Better prompt: review the AI-generated dashboard changes first. Look for bugs, security issues, data flow mistakes, missing tests, accessibility gaps, performance problems, and code that is hard to maintain. List review findings by severity. Do not refactor until blockers are fixed. After that, extract repeated card markup into one component without changing behavior, then run the dashboard test and production build.
The better prompt separates review findings from cleanup and makes refactor safely part of a verified workflow.
FAQ
Q: Should every vibe coded change get a code review? A: Important changes should. Public pages, auth, payments, database writes, API routes, user data, and SEO pages deserve review before launch.
Q: When is it safe to refactor AI-generated code? A: Refactor after behavior is verified. If no automated test exists, use a precise manual path and keep the refactor small.
Q: What should reviewers prioritize? A: Prioritize correctness, security, data ownership, validation, accessibility, performance, and maintainability before naming or style preferences.
Q: Can AI review its own code? A: It can help, but do not treat that as final approval. Use AI to surface risks, then verify findings and fixes with tests, build output, or human review.