<ul><li>RAG comprises two primary components: a retriever and a generator, aimed at retrieving and generating information, respectively.</li><li>External memory sources like databases, conversations history, or internet data are utilized by the retriever to provide relevant information for the generator.</li><li>RAG enhances the model's knowledge by accessing updated information, contributing to contextually relevant responses.</li><li>The success of a RAG system heavily relies on the quality of its retriever, with sparse and dense retrievers being key types that handle data representation differently.</li></ul>

RAG Basics in 4 Minutes: From Retrieval to Generation

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