Code reviews are essential for maintaining code quality as teams grow, but they can create bottlenecks and conflicts without proper guidelines. Effective code reviews require both authors and reviewers to follow best practices: authors should keep changes small, self-review first, and provide clear descriptions, while reviewers should respond within 24 hours, focus on constructive feedback, and approve when code is good enough rather than perfect. The typical workflow involves creating pull requests, running automated checks through CI, conducting human reviews, making updates based on feedback, and deploying approved changes. AI tools like CodeRabbit can complement human reviewers by handling routine checks and providing consistent feedback, ultimately improving developer velocity while maintaining code quality.
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