code-review-checklist

A comprehensive code review checklist should cover correctness, security, performance, code quality, testing, and documentation to ensure systematic quality assurance and security hardening before merging.

To review AI code, check for specific AI and LLM patterns like handling hallucinations and preventing prompt injection, while verifying standard security and performance criteria using a structured checklist.

Common code anti-patterns are flawed structural design choices that violate best practices; identifying them during code review helps suggest better alternatives and ensures long-term maintainability.

Categorize code review comments using a clear guide that separates feedback into blocking issues, suggestions, nits, and questions, facilitating systematic quality assurance and targeted improvements.

Yes, a code review checklist can be used for both general software engineering and AI-specific projects, providing detailed checks for standard code quality alongside specific guidelines for AI logic.

The best way to ensure code quality and security before a merge is to apply a structured code review checklist that verifies correctness, performance, testing, and documentation systematically.