RETRIEVAL AUGMENTED GENERATION (RAG): ENHANCING THE ACCURACY, RELIABILITY, AND TRUSTWORTHINESS OF LARGE LANGUAGE MODELS FOR INTELLIGENT DECISION SUPPORT

Abstract

The advent of Large Language Models (LLMs) has been a game-changer in the realm of Artificial Intelligence, showcasing its prowess in natural language processing, knowledge creation, and automated decision-making. Although they show impressive capabilities, LLM-based systems still have significant issues with hallucination, outdated information, limited domain-specific knowledge, and a lack of transparency, making them unreliable for intelligent decision-support systems. The recent trend of Retrieval-Augmented Generation (RAG) is an innovative solution that combines external knowledge retrieval mechanisms with generative AI models to ensure the accuracy, reliability, and trustworthiness of AI responses. The study seeks to explore how well RAG can improve the performance of LLM-powered systems, focusing on its contributions to factual accuracy, grounding, preventing hallucinative responses, and decision-making support. A qualitative research method using systematic analysis of recent scholarly literature is taken to gain insight into theoretical and practical evolution of RAG architectures. The results show that RAG is a highly effective method to enhance LLM performance by providing domain-specific, up-to-date, and trustworthy information sources. In addition, RAG provides greater transparency, helps users trust the AI-generated results, and minimizes unsupported outputs. There are challenges that need to be addressed in the future concerns of retrieval quality, computational complexity, data privacy, scalability, and the evaluation standards. The research underscores the importance of RAG as a major step forward in ensuring the reliability of AI-driven decision-making, moving beyond generative capabilities to a more trustworthy approach. The findings from the research are applicable in various fields, including healthcare, education, finance, and enterprise management, and can be used to create reliable and accurate decision-support systems powered by AI.