AI Code Review Checklist - Checklist Workbook

Master AI-assisted code reviews with this structured checklist workbook. Designed for developers using tools like GitHub Copilot and ChatGPT, it covers 8 critical areas to catch bugs, enforce standards, and improve collaboration.

Checklist

• Verify AI-generated code adheres to project-specific naming conventions and style guides (e.g., PEP 8, ESLint rules).
• Check for correct implementation of error handling and edge cases—AI often misses null checks or boundary conditions.
• Confirm all external API calls include proper authentication tokens and rate limiting (e.g., using OAuth 2.0 or API keys).
• Audit for security vulnerabilities: SQL injection, XSS, or hardcoded secrets—especially in AI-suggested snippets.
• Ensure code is modular and follows DRY principles; refactor AI outputs that duplicate logic across functions.
• Test for performance bottlenecks: AI may generate inefficient loops or unnecessary database queries (e.g., N+1 problems).
• Validate documentation completeness: AI comments should explain 'why' not just 'what', and include usage examples.
• Run automated tests (unit, integration, regression) on AI contributions to catch regressions before merging.

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Frequently Asked Questions