Why you need this
AI code assistants like GitHub Copilot, ChatGPT, and Claude can generate code in seconds—but that doesn't mean it's production-ready. AI-generated code can contain security vulnerabilities, performance issues, logic errors, and subtle bugs that won't surface until runtime.
The problem: Teams are shipping AI-generated code without proper review processes, leading to security incidents, technical debt, and production failures.
This checklist solves that. It provides a systematic framework for reviewing AI-generated code safely, catching issues before they hit production.
Perfect for:
- Software engineers using AI coding assistants
- Engineering managers establishing code review standards
- Security teams auditing AI-generated code
- DevOps teams integrating AI tools into CI/CD pipelines
What's inside
Comprehensive Review Framework
Pre-Integration Checks:
- Verify functionality against requirements
- Test edge cases and error handling
- Check for security vulnerabilities (SQL injection, XSS, command injection)
- Review input validation and sanitization
- Assess error messages for information leakage
Code Quality Assessment:
- Evaluate code readability and maintainability
- Check naming conventions and documentation
- Review complexity and adherence to SOLID principles
- Verify proper resource management (memory leaks, file handles)
- Assess test coverage and quality
Security Deep Dive:
- Authentication and authorization checks
- Secrets management (no hardcoded credentials)
- Data encryption and privacy compliance
- Dependency scanning for known vulnerabilities
- API security best practices
Performance & Scalability:
- Algorithm efficiency analysis
- Database query optimization
- Caching strategy review
- Load testing recommendations
- Resource consumption profiling
Each Checklist Item Includes:
- ✓ Clear verification criteria
- ✓ Common AI code generation mistakes to watch for
- ✓ Remediation steps when issues are found
- ✓ Tool recommendations for automated checking
How to use it
- During code reviews — Use as a systematic checklist before approving AI-generated pull requests
- Team onboarding — Train new developers on safe AI code usage from day one
- CI/CD integration — Convert checklist items into automated tests and linting rules
- Security audits — Conduct periodic reviews of code generated by AI assistants
Example checklist item
Security Check: SQL Injection Prevention
❌ Common AI mistake:
query = f"SELECT * FROM users WHERE id = {user_id}"
cursor.execute(query)
✅ Correct approach:
query = "SELECT * FROM users WHERE id = ?"
cursor.execute(query, (user_id,))
Verification: Ensure all database queries use parameterized statements. No string concatenation with user input.
Tools: Use SQLMap, Bandit (Python), or similar static analysis tools to detect vulnerabilities.
Want to go deeper?
This checklist covers the essentials. For comprehensive guidance on working safely with AI coding tools:
- Guide: AI at Work Basics — Best practices for AI tools in professional settings
- Guide: Prompting 101 — Get better code suggestions from AI assistants
- Glossary: AI — Understanding AI capabilities and limitations
License & Attribution
This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You're free to:
- Share with your engineering team
- Adapt for your organization's coding standards
- Integrate into internal documentation
Just include this attribution:
"AI Code Review Checklist" by Field Guide to AI (fieldguidetoai.com) is licensed under CC BY 4.0
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