<ul><li>RAG (Retrieval-Augmented Generation) enhances LLMs by grounding them in external knowledge sources like specified documents such as code files.</li><li>It allows AI systems to retrieve relevant information from the codebase before generating answers, improving accuracy and reliability.</li><li>Setting up a RAG system for a codebase involves steps like creating a Vertex AI RAG Corpus, importing staged code files, defining tools for models like Gemini, and using SDK clients for queries.</li><li>Vertex AI RAG Engine provides a managed platform for handling data ingestion, vectorization, indexing, and retrieval, transforming how developers interact with and understand code.</li></ul>

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