Large Language Model Integration for Enterprise Knowledge Management Platforms

Abstract

Enterprise Knowledge Management Platforms (EKMPs) help organizations capture, organize, share, and utilize knowledge to improve decision-making and operational efficiency. Traditional knowledge management systems often face challenges such as data silos, unstructured information, limited contextual understanding, and ineffective search capabilities. The integration of Large Language Models (LLMs) addresses these limitations by enabling intelligent search, semantic understanding, automated content generation, and conversational interfaces. This study proposes an LLM-powered knowledge management framework that combines Retrieval-Augmented Generation (RAG), semantic embeddings, enterprise-specific language models, and vector databases to transform enterprise data into actionable knowledge. The framework includes data ingestion pipelines, knowledge repositories, embedding generation modules, retrieval systems, and conversational AI interfaces while emphasizing security, privacy, governance, scalability, and explainability. It also addresses challenges such as hallucination reduction, domain adaptation, and knowledge freshness. Experimental results demonstrate that LLM-based systems significantly improve retrieval accuracy, response relevance, knowledge reuse, and user satisfaction compared with traditional keyword-based approaches. These advancements enhance employee productivity, decision quality, collaboration, and innovation, positioning LLM-driven knowledge management systems as a key enabler of enterprise digital transformation.