How would you review AI-generated code before merging it into the main branch?

Review AI-generated code the same way you would review code written by a human—but with extra attention to correctness and maintainability. AI can produce code that appears convincing while introducing subtle bugs, security issues, or unnecessary complexity.

A practical review process looks like this:

1. Understand the intent
   1. Read the requirements or user story.
   2. Verify that the generated code actually solves the intended problem.
   3. Check that edge cases and error scenarios are handled.
2. Review the code manually
   1. Ensure the logic is correct.
   2. Look for duplicated or overly complex code.
   3. Confirm naming, formatting, and architecture follow your team's standards.
   4. Remove unnecessary comments or AI-generated boilerplate.
3. Check security
   1. Validate all inputs.
   2. Look for SQL injection, XSS, CSRF, command injection, and path traversal risks where applicable.
   3. Ensure secrets, API keys, or credentials are not hardcoded.
   4. Verify authentication and authorization logic.
4. Run automated tests
   1. Execute existing unit, integration, and end-to-end tests.
   2. Add new tests for newly introduced functionality.
   3. Verify adequate code coverage.
5. Perform static analysis
   1. Run linters and formatters.
   2. Use static analysis tools to identify bugs and code smells.
   3. Check dependency vulnerabilities if new packages were added.
6. Evaluate performance
   1. Review algorithms for unnecessary complexity.
   2. Watch for inefficient database queries (e.g., N+1 queries).
   3. Check memory usage and concurrency issues where relevant.
7. Verify maintainability
   1. Ensure the code is modular and readable.
   2. Confirm it aligns with existing design patterns.
   3. Remove dead code and unused dependencies.
8. Inspect AI-specific issues
   1. Verify that referenced APIs and libraries actually exist.
   2. Check that generated examples aren't outdated.
   3. Confirm licensing if code closely resembles third-party sources.
   4. Be skeptical of code that "looks right" but lacks explanation.
9. Test in a staging environment
   1. Deploy to staging.
   2. Perform functional and regression testing.
   3. Monitor logs for unexpected errors.
10. Complete the pull request review
    1. Ensure all CI checks pass.
    2. Require at least one human reviewer.
    3. Merge only after addressing all review comments.

Sample AI Code Review Checklist

• Meets the functional requirements
• Handles edge cases and invalid inputs
• Passes all automated tests
• No security vulnerabilities
• No performance regressions
• Follows coding standards
• No unnecessary dependencies
• Documentation updated if behavior changed
• CI/CD pipeline passes
• Human approval obtained before merge

Best practice: Treat AI as a coding assistant, not an authority. Every AI-generated change should undergo the same rigorous review, testing, and approval process as any other contribution before being merged into the main branch.