Enhancing Enterprise Knowledge Management through Chain-of-Thought Prompting in Large Language Models: A Framework for Secure and Intelligent Decision Support

Abstract

The rapid exponential growth of big data has rendered traditional Knowledge Management (KM) systems insufficient for real-time decision-making. While organizations possess vast repositories of unstructured data, the ability to synthesize this information into actionable insights remains a critical bottleneck. This study explores the integration of Large Language Models (LLMs) into enterprise KM frameworks, specifically focusing on the efficacy of Chain-of-Thought (CoT) prompting and Active Prompting strategies to enhance reasoning fidelity. Drawing upon recent advancements in foundation models, including GPT-4 and LLaMA, we propose a novel architecture that couples generative AI with Role-Based Access Control (RBAC) protocols to ensure data security and governance. Our methodology involves a comparative analysis of zero-shot reasoning versus iterative CoT prompting across simulated business intelligence scenarios. The results indicate that CoT prompting significantly mitigates logical fallacies and improves the contextual relevance of outputs, effectively transforming LLMs from passive text generators into active reasoning engines. Furthermore, the integration of RBAC mechanisms addresses the critical challenge of information segregation in hierarchical organizations. This research suggests that synergizing advanced prompting engineering with robust security standards allows organizations to leverage their data assets more effectively, fostering a "learning organization" culture that supports competitive advantage.