🚀 AI Code Review: Where It Helps and Where It Doesn’t

Short summary

An overview of where AI-powered code review tools like GitHub Copilot, ChatGPT, Claude, and Gemini excel and where they fall short. AI is effective at spotting code smells, common bugs, security risks, and readability issues, but struggles with business context, distributed system dependencies, and team-specific conventions. The recommended approach is running AI review first for routine checks, then submitting for human review focused on architecture and business logic.

• •AI tools excel at catching code smells, common bugs, security risks, and readability improvements
• •AI struggles with business context, cross-service dependencies, architectural trade-offs, and team conventions
• •Best practice: run AI review first, then human review for business logic, architecture, and edge cases

Generated with AI, which can make mistakes.