The Hallucination Challenge: Strategies for Ensuring Factuality in AI-Driven Knowledge Management Systems.

This conceptual review sets out to explore and offer a framework in order to tackle the new challenge of AI hallucination in Knowledge Management Systems (KMS). Large Language Models (LLMs) are predicted to affect the creation of knowledge, and they frequently produce content that is not true or even makes up. Raising questions on the integrity and reliability of organizational knowledge, user trust and the reliability of information that is crucial for effective decision making. The paper demonstrates how the existing approaches to establish factual accuracy in the current system are disjoint and distributed between technical and organizational aspects, based on theoretical concepts in artificial intelligence, information systems and knowledge management. Hence in this paper, a hybrid architecture for factuality assurance has been proposed, which does not involve single technical intervention. The proposed framework is aligned with evidence-based retrieval protocols, powerful organizational oversight, explainability protocols, and human-in-the-loop approaches, thus offering a socio-technical approach to AI reliability. This research adds to Knowledge Management theory by moving factuality assurance from being a model-performance competency to a competency of governance. The chapter provides practitioners and policymakers with insights into how to build trust between the knowledge that is produced in organizations and the technology that is rapidly changing, thus enhancing the resilience of knowledge systems powered by artificial intelligence.

Artificial Intelligence, Hallucination, Knowledge Management, Retrieval-Augmented Generation, Organizational Governance

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