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Explore how to build sophisticated AI-powered code review systems through advanced context engineering techniques in this 32-minute conference talk. Learn how CodeRabbit's architecture integrates multiple data sources including AST-based code dependency graphs, Model Context Protocol (MCP), over 40 open-source linters, repository history, coding agent guidelines, and custom review rules to deliver comprehensive code analysis. Discover methods for detecting race conditions, identifying architectural improvements, catching missed unit tests particularly for edge cases, spotting concurrency issues, and finding errors and bugs that exist outside the immediate code diff. Gain insights into leveraging large language models and open-source DevOps tooling to create context-aware systems that provide more accurate and valuable automated code reviews than traditional static analysis tools.
