Context-Aware LLM-RAG for Enterprise Decision Support
Keywords:
Context-Preserving Generative AI,Enterprise Knowledge Workflows,LLM-RAG Framework,Retrieval-Augmented Generation (RAG),Real-Time Decision Support,Management Decision Intelligence,Enterprise Knowledge Management (EKM),Semantic Search,Hybrid Retrieval (Sparse + Dense),Contextual Grounding,Policy-Aware Generation,Role-Based Access Control (RBAC),Data Governance & Compliance,Human-in-the-Loop (HITL),Explainability & Provenance Tracking.Abstract
Enterprises generate large volumes of unstructured information in reports, presentations, and conversations. This information often resides in diverse, distributed data sources, limiting its use for direct decision-making and leading to the adoption of adhoc workflows. The delay, inaccuracy, and poor governance of such work increase risk. A variety of solutions have sought to improve the integration of knowledge work into enterprise processes.
A Retrieval-Augmented Generation framework, augmented with a context-preserving knowledge-retrieval layer, provides decision-support capabilities across preset operation, logistics, planning, and compliance domains. Tools, interfaces, and monitoring dashboards allow for real-time context creation, approval, and rollback during LLM inference. Data provenance tracks root sources, promotes trust in responses, and accelerates compliance audits.
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