Abstract

Retrieval-Augmented Generation (RAG) has emerged as a significant framework for improving intelligent enterprise knowledge management by combining information retrieval with large language models (LLMs). Traditional enterprise knowledge-management systems often struggle with fragmented information, rapidly changing organizational knowledge, limited contextual understanding, and difficulties in retrieving relevant information from heterogeneous data sources. RAG addresses these challenges by retrieving relevant documents, records, or knowledge artifacts from enterprise repositories and incorporating them into the context supplied to a generative model. This approach can improve the relevance, factual grounding, traceability, and contextual accuracy of generated responses while reducing dependence on static model knowledge. This paper examines RAG frameworks for enterprise knowledge management, focusing on their architecture, retrieval mechanisms, knowledge representation, indexing strategies, document processing, embedding models, vector databases, reranking techniques, and generation components. It also discusses hybrid retrieval approaches that combine semantic and keyword-based search to support diverse enterprise information needs. Particular attention is given to challenges involving data privacy, access control, knowledge freshness, hallucination, scalability, interoperability, and evaluation