Retrieval-Augmented Generation (RAG) enhances the efficiency of information retrieval, allowing users to query vast document sets in seconds rather than hours.
One key design decision for RAG systems involves choosing between local or cloud deployment, each offering distinct advantages in cost and privacy.
The knowledge database, or vector database, is crucial for RAG, created by embedding models that group similar documents in a vector space for efficient querying.
In a RAG system, the LLM acts as a highly efficient library clerk, processing and answering queries based on the retrieved knowledge.
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