AI-Assisted Code Review Checklist

Overview

This checklist ensures AI-generated code meets quality standards and follows team best practices. Use it for both AI-assisted reviews and human reviews of AI-generated code.

Pre-Review Setup

✅ Context Preparation

AI has access to relevant files and context
Review scope is clearly defined
AI understands project requirements
Relevant documentation is available
Previous review feedback is considered

🤖 AI Configuration

Cursor Rules are up-to-date
AI has proper project context
Review guidelines are clear
Quality thresholds are set
Review focus areas are specified

Code Quality Review

🏗️ Architecture & Design

Code follows established patterns
Separation of concerns is maintained
Dependencies are properly managed
Code is modular and reusable
Design principles are followed (SOLID, DRY, etc.)

📝 Code Style & Standards

Follows team coding standards
Consistent naming conventions
Proper formatting and indentation
No code duplication
Functions are focused and small

🔒 Security & Safety

Input validation is implemented
Authentication/authorization is proper
Sensitive data is protected
SQL injection is prevented
XSS vulnerabilities are avoided
Rate limiting is implemented
Error messages don’t leak information

🧪 Testing & Coverage

Unit tests are present and passing
Test coverage meets requirements (>80%)
Edge cases are tested
Error scenarios are covered
Integration tests are included
Test data is properly managed

AI-Specific Review Items

🤖 AI Generation Quality

Code is logically sound
No hallucinated APIs or libraries
Generated code follows best practices
AI didn’t introduce anti-patterns
Code is maintainable and readable

🔍 Context Understanding

AI understood the requirements correctly
Generated code fits the existing architecture
No conflicts with existing patterns
Dependencies are properly identified
Integration points are correct

📚 Documentation & Comments

Complex logic is explained
API endpoints are documented
Configuration options are clear
Usage examples are provided
Comments explain “why” not “what”

Performance & Scalability

⚡ Performance

No obvious performance bottlenecks
Efficient algorithms are used
Database queries are optimized
Caching is implemented where appropriate
Resource usage is reasonable

📈 Scalability

Code can handle increased load
No hardcoded limits that could cause issues
Resource usage scales appropriately
Database connections are managed properly
Async operations are handled correctly

Error Handling & Resilience

🚨 Error Handling

Errors are caught and handled gracefully
User-friendly error messages are provided
Errors are logged for debugging
Fallback mechanisms are in place
Error states don’t crash the application

🔄 Resilience

Code handles edge cases gracefully
Network failures are handled
Timeout scenarios are considered
Retry logic is implemented where appropriate
Circuit breakers are used for external services

Accessibility & User Experience

♿ Accessibility

ARIA labels are properly implemented
Keyboard navigation is supported
Color contrast meets standards
Screen reader compatibility is ensured
Focus management is proper

👥 User Experience

Loading states are implemented
Error states are user-friendly
Success feedback is provided
User input is validated clearly
Responsive design is maintained

Compliance & Standards

📋 Industry Standards

Follows language/framework best practices
Meets industry security standards
Complies with relevant regulations
Uses recommended libraries and tools
Follows established design patterns

🏢 Team Standards

Follows team coding guidelines
Meets project-specific requirements
Uses established naming conventions
Follows team architecture patterns
Meets team quality thresholds

Review Process

🔍 Review Execution

AI review is completed first
Human review validates AI findings
All checklist items are reviewed
Issues are documented clearly
Priority levels are assigned to issues

📝 Feedback Documentation

Issues are clearly described
Suggested fixes are provided
Context and reasoning are explained
Examples are given where helpful
Follow-up actions are specified

✅ Review Completion

AI Review Prompts

🎯 Effective Review Prompts

Use these prompts to get better AI reviews:

General Review:

Review this code for quality, security, and best practices. Focus on:
- Code quality and maintainability
- Security vulnerabilities
- Performance issues
- Testing coverage
- Documentation quality

Security-Focused Review:

Perform a security-focused code review. Check for:
- Input validation issues
- Authentication/authorization problems
- Data exposure risks
- Injection vulnerabilities
- Security best practices

Performance Review:

Review this code for performance issues. Look for:
- Inefficient algorithms
- Database query problems
- Memory leaks
- Resource usage issues
- Optimization opportunities

Architecture Review:

Review the architecture and design of this code. Consider:
- Design patterns used
- Separation of concerns
- Code organization
- Maintainability
- Scalability

Quality Gates

🚦 Review Approval Criteria

Must Pass:

All critical security issues resolved
All critical bugs fixed
Test coverage meets minimum requirements
Code follows team standards
Performance meets requirements

Should Pass:

All major issues resolved
Documentation is complete
Code is well-structured
Error handling is comprehensive
Accessibility requirements met

Nice to Have:

Code is optimized for performance
Additional tests are included
Documentation is exemplary
Code follows best practices perfectly
Performance exceeds requirements

Post-Review Actions

📋 Issue Resolution

All issues are tracked in issue management system
Priority levels are assigned
Assignees are designated
Deadlines are set
Progress is monitored

🔄 Follow-up

Code changes are reviewed after fixes
Quality gates are re-checked
Performance impact is measured
User feedback is collected
Lessons learned are documented

Continuous Improvement

📊 Metrics Tracking

🎯 Process Improvement

Review process is regularly evaluated
AI review effectiveness is measured
Team feedback is collected
Process improvements are implemented
Best practices are shared

Template Usage

📝 How to Use This Checklist

Copy this template for each review
Customize based on project needs
Use AI assistance to complete reviews faster
Validate AI findings with human review
Track progress through completion
Share learnings with the team

🔄 Customization

Add project-specific requirements
Adjust quality thresholds
Include team-specific standards
Add compliance requirements
Modify review focus areas

This checklist is a living document. Update it based on team feedback and project requirements.