Context engineering is emerging as a crucial differentiator in AI applications, with an emphasis on understanding and utilizing project-specific architecture, patterns, and goals to provide valuable code reviews. CodeRabbit exemplifies the importance of context by employing a non-linear review pipeline that collects and verifies context data from diverse sources, ensuring alignment with the overall PR and codebase context. Effective context engineering distinguishes AI code review tools that merely match generic coding patterns from those that deliver insightful and project-relevant feedback. Challenges in providing the right context include avoiding hallucinations from insufficient context, preventing overload from excessive context, and ensuring a balanced input for AI to derive accurate insights. CodeRabbit addresses these challenges by developing a multilayered context preparation approach that maximizes AI understanding through advanced information collection, filtering, and structuring. This includes metadata collection, differential analysis, path filtering, past insights reuse, and code graph analysis, among others, to maintain high-quality reviews that consider architectural inconsistencies and performance bottlenecks. The process culminates in a review guide that incorporates coding guidelines and past useful comments, ensuring that AI agents provide feedback aligned with team-specific best practices. This method enhances productivity, robust code development, and team efficiency, while the continual evolution of models promises further improvements in AI code review capabilities.