Gen-AI for Knowledge Management: Automated Knowledge Base Creation and Context-Aware Q&A Systems

Abstract

Introduction: Knowledge Management (KM) is critical for organizations to capture, organize, and retrieve information efficiently. Generative AI is revolutionizing KM by enabling automatic creation of knowledge content and highly intelligent query answering systems.

Objectives: This paper explores two interrelated advancements: (1) Automated Knowledge Base Creation, where generative models summarize and distill information from large corpora or documentation into structured knowledge articles or knowledge graphs, reducing the manual effort of building knowledge repositories; and (2) Context-Aware Q&A Systems, where large language models (LLMs) deliver precise answers to user queries by understanding context and retrieving relevant knowledge, effectively serving as intelligent assistants that can reason over an organization’s data.

Methods: We examine state-of-the-art techniques such as retrieval-augmented generation (RAG) for grounding AI answers in enterprise data blogs.nvidia.comblogs.nvidia.com, and outline how open-source tools (like Haystack, LangChain) and platforms (IBM Watson, GPT-4 with plugins) implement these functionalities.

Results: The paper also addresses the challenges of maintaining knowledge accuracy, avoiding hallucinations, ensuring security of proprietary information, and mitigating bias in AI-generated knowledge. Through case studies and literature review, we demonstrate how generative AI-based KM solutions can significantly improve information discovery and decision-making, by providing up-to-date, contextually relevant knowledge on demand.

Conclusions: We conclude with future directions, such as integrating knowledge graphs with LLMs for reasoning and the evolving role of human knowledge curators in an AI-augmented knowledge lifecycle.