Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by connecting them to external knowledge sources. Unlike general-purpose LLMs like ChatGPT, RAG allows AI assistants to answer questions about specific, private information, such as internal project details, by retrieving relevant data and providing it alongside the user's query. This process involves preparing knowledge by loading documents, chunking them into smaller pieces, creating numerical representations called embeddings to capture meaning, and storing these in a vector database for efficient searching. AI
IMPACT RAG enables LLMs to access and utilize private or domain-specific data, expanding their utility beyond general knowledge.
RANK_REASON The item explains a technical concept (RAG) and its application, rather than announcing a new product or research breakthrough.
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