Retrieval-Augmented Dashboards: Enabling Context-Aware Analytics through LLM Integration with BI Platforms

Abstract

Business intelligence (BI) platforms have traditionally excelled at visualizing structured data but often fall short in providing contextual depth. This article explores the integration of Large Language Models with BI systems through Retrieval-Augmented Generation (RAG) frameworks to address this limitation. By dynamically connecting quantitative metrics with relevant contextual information from unstructured sources, these enhanced dashboards enable more comprehensive decision-making. A compliance intelligence implementation demonstrates the practical application in regulatory environments, with users experiencing improved analytical efficiency and insight quality. The architecture incorporates document indexing, query processing, relevance ranking, and context synthesis components working in concert to deliver contextually enriched visualizations. Despite implementation challenges related to latency management and knowledge base maintenance, the comparative advantages justify continued development across healthcare, financial, and manufacturing sectors. The transformative potential of this approach lies in its ability to bridge the historical divide between data analysis and contextual interpretation, fundamentally altering how organizations derive insights from increasingly diverse information ecosystems.