Retrieval-Augmented Generation (RAG) is a technique that allows AI models to answer questions using specific documents without needing to retrain the model. The RAG process involves two main pipelines: indexing, where documents are processed into searchable chunks with embeddings, and answering, where relevant chunks are retrieved to ground the AI's response. This method is crucial for applications like internal HR assistants, where models need to access and utilize company-specific information accurately and efficiently. AI
IMPACT Enables AI to accurately answer questions using specific, up-to-date documents without costly model retraining.
RANK_REASON The item explains a technical concept (RAG) and its implementation details, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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