CodeRabbit has developed an advanced context engineering approach to enhance AI-driven code reviews by integrating extensive contextual data into their review process. This method involves utilizing a 1:1 ratio of code-to-context in Large Language Model (LLM) prompts, drawing from various sources such as Jira tickets, code graphs, past pull requests (PRs), and static analyzers to provide a comprehensive understanding of the codebase. CodeRabbit's system ensures security by cloning repositories in isolated sandboxes and performs detailed analyses of code dependencies and file relationships. It includes custom review instructions to align with team-specific coding standards and employs over 40 linters and static analysis tools to detect potential bugs. The platform also performs real-time web queries to provide up-to-date information and runs verification scripts to filter out low-value comments, thereby reducing AI hallucinations and enhancing the accuracy and relevancy of code reviews. By understanding the intent behind code changes and maintaining a high signal-to-noise ratio in feedback, CodeRabbit aims to deliver precise and reliable code reviews.