Leveraging RAG for Enhanced Business Intelligence with Local LLMs

This research addresses critical limitations in current business intelligence (BI) insights generation. It focuses on real-time data updates, accuracy, data privacy, security and more to build a system that is practically viable for modern-day businesses. Leveraging the proposed solution gives organizations a competitive edge in the fast-paced market. Research leverages emerging Large Language Models (LLMs) to derive relevant actionable insights while eliminating typical hallucinations. The research proposes system architecture that combines Retrieval-Augmented Generation (RAG) technology with local LLMs and real-time data streaming via Kafka to ensure data privacy, factual accuracy, and timely insights. Research presents quantitative and qualitative analysis for insights generation based on RAG vs. Direct LLM request. Moreover, research is executed on two different families of LLMs - thinking models and traditional models for detailed validation. Empirical testing reveals that RAG-based approaches outperform direct LLM queries in response time, CPU efficiency, and factual accuracy for insights generation. By leveraging local LLMs instead of cloud-based solutions, research aims to protect data and intellectual property of organizations.