ENHANCING INFORMATION RETRIEVAL WITH RETRIEVAL-AUGMENTED GENERATION (RAG) FOR IMPROVED CONVERSATIONAL AI

Abstract

This article presents a comprehensive analysis of Retrieval-Augmented Generation (RAG) systems and their application in enhancing conversational AI capabilities. It proposes an integrated framework that combines traditional information retrieval techniques with state-of-the-art language models to improve response accuracy and contextual relevance in AI-powered chatbots and virtual assistants. This article addresses key challenges in knowledge retrieval and response generation through an optimized architecture that dynamically retrieves and synthesizes information from large-scale knowledge bases. The experimental results demonstrate significant improvements in response quality, factual accuracy, and contextual appropriateness compared to conventional approaches. It also introduces novel optimization strategies for managing latency and scaling challenges in production environments. It also suggests that RAG-based systems represent a promising direction for developing more capable and reliable conversational AI applications, particularly in domains requiring precise and up-to-date informatison retrieval.