Intelligent Enterprise Knowledge Management via Domain-Specific Retrieval-Augmented Generation and User-Centric Query Understanding

Abstract

Modern enterprises face an escalating challenge in managing and leveraging vast, heterogeneous knowledge repositories while ensuring that information retrieval and synthesis align precisely with end-user intent. This paper presents a systemic examination of intelligent enterprise knowledge management through the synthesis of domain-specific retrieval-augmented generation (RAG) and user-centric query understanding. We propose a conceptual architecture that integrates fine-grained query interpretation, contextual disambiguation, and dynamic retrieval from curated document bases with large language model (LLM) generation to produce accurate, traceable, and contextually grounded responses. The discussion extends beyond algorithmic design to encompass the structural trade-offs between retrieval precision and generative fluency, the governance of proprietary knowledge, infrastructure scalability, and deployment sustainability. We analyze how user-centric query modeling—incorporating role-based, task-oriented, and temporal signals—can mitigate hallucinations and improve relevance in high-stakes enterprise settings. Further, we address cross-domain fairness, robustness against distributional shifts, and the policy implications of automated knowledge synthesis. Through a multidisciplinary lens, we explore how RAG systems can be hardened for production through continuous feedback loops, audit trails, and ethical guardrails. The paper contributes a holistic framework for designing knowledge management systems that balance generative capability with retrieval accountability, ensuring that enterprises can harness AI-driven insights without sacrificing reliability, compliance, or user trust. We conclude with a roadmap for future research that emphasizes human-in-the-loop validation, federated knowledge indexing, and the alignment of retrieval objectives with organizational values.