Context-Aware Retrieval-Augmented Generation for Enterprise Knowledge Management
Keywords:
context-aware retrieval, retrieval-augmented generation, enterprise knowledge management, large language models, governance, semantic retrieval, infrastructureAbstract
Enterprise knowledge management increasingly depends on generative artificial intelligence to transform unstructured organizational information into actionable insight. Retrieval-augmented generation has emerged as a promising architectural strategy because it grounds language model outputs in external evidence, thereby reducing hallucination and enabling traceability. However, applying retrieval-augmented generation in enterprise settings introduces challenges that extend well beyond open-domain question answering. Organizational knowledge is distributed across heterogeneous systems, governed by complex access controls, subject to regulatory constraints, and shaped by rapidly shifting operational context. This paper presents a system-level analysis of context-aware retrieval-augmented generation for enterprise knowledge management. It examines the conceptual foundations of retrieval-augmented generation, the meaning of context in enterprise retrieval, architectural trade-offs in indexing and retrieval infrastructure, governance and compliance requirements, fairness and robustness concerns, deployment considerations, and evaluation strategies. The analysis emphasizes structural, organizational, and policy dimensions rather than focusing narrowly on model performance. Context-aware retrieval is framed as a socio-technical capability that must reconcile relevance, authority, latency, cost, security, and auditability. The paper argues that sustainable enterprise systems require modular architectures, fine-grained access-aware indexing, continuous evaluation under shifting knowledge distributions, and governance mechanisms that link retrieval behavior to organizational accountability. Future research directions include interoperable knowledge graphs, adaptive retrieval policies, energy-aware inference, and cross-enterprise federated retrieval under heterogeneous regulatory regimes.
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