This whitepaper explains how retrieval-augmented generation (RAG) improves enterprise search by combining a retriever that finds the most relevant snippets from large, mostly unstructured repositories with a generative model that produces answers grounded in those retrieved sources, reducing the weaknesses of keyword search and lowering hallucination risk (pages 7–10). It argues enterprises are overwhelmed by unstructured data (often cited as the majority of organizational data) and traditional tools miss context, synonyms, and intent, which wastes time and drives incomplete decisions (pages 3–4). It outlines core RAG building blocks such as tokenization, embeddings, document and query encoders, vector search over an indexed corpus, ranking, generation, and feedback loops, and highlights v

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