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Every engineering team loses years of knowledge when developers leave. I found a fascinating solution to this problem. In this video, I demonstrate how AI can learn from your team's actual code review patterns and decisions - not generic best practices, but YOUR specific standards. What I cover: → The institutional knowledge problem in software teams → Live demo: Custom AI reviewers trained on real PR history → GitHub integration catching team-specific issues → Real-time code review in VS Code/Cursor (no PR needed!) → Testing review criteria before production → Free prompt library for better code reviews 🔧 Tools mentioned: Baz Custom Reviewers: https://baz.co/custom-reviewers?utm_s... Baz MCP for Cursor/VS Code AwesomeReviewers prompt collection This isn't about replacing code reviews - it's about scaling your team's best practices infinitely. 💡 Key insight: Your rejected PRs and review comments are more valuable than your documentation. They contain your team's real engineering philosophy. 🎯 Perfect for: Engineering managers dealing with knowledge silos Teams using AI coding assistants Anyone tired of explaining the same patterns repeatedly Developers interested in AI-augmented workflows What's your biggest challenge with maintaining code quality standards? Let me know in the comments. #CodeReview #AITools #SoftwareEngineering #DeveloperTools #Baz #MCP #GitHubIntegration #EngineeringLeadership #TechLeadership #CodingStandards