Implementing Retrieval Augmented Generation for Intelligent Enterprise Cloud Information Systems

Abstract

The rapid evolution of cloud computing and artificial intelligence has significantly transformed enterprise information systems by enabling organizations to process, analyze, and utilize large volumes of digital information. However, conventional enterprise systems often face challenges related to fragmented knowledge repositories, inefficient information retrieval, limited contextual understanding, and difficulties in generating accurate business insights. Retrieval Augmented Generation (RAG) has emerged as an advanced artificial intelligence approach that combines information retrieval mechanisms with large language models to improve the accuracy, relevance, and reliability of generated responses. Implementing RAG within enterprise cloud information systems enables organizations to integrate internal knowledge sources, operational databases, and external information resources into intelligent decision-support environments. Cloud platforms provide the scalability, computational capacity, security mechanisms, and flexibility required for deploying and managing RAG-based applications at enterprise scale. This research examines the implementation of Retrieval Augmented Generation for intelligent enterprise cloud information systems by exploring architectural components, technological requirements, deployment strategies, security considerations, and organizational benefits. The study investigates how RAG-enabled cloud systems improve knowledge management, automation, decision-making, customer service, and operational efficiency. Through a systematic analysis of existing research and technological frameworks, the study develops insights into designing scalable, adaptive, and trustworthy enterprise information systems capable of supporting modern digital transformation initiatives