Retrieval-Augmented Generation has evolved from a research concept into a fundamental architecture pattern for enterprise AI systems. RAG addresses the inherent limitations of standalone large language models by incorporating external knowledge sources into the generation process, creating a hybrid approach that combines parametric knowledge learned during training with non-parametric knowledge retrieved at inference time.Advanced AI systems in Cloud environments leverage this architectural pattern to deliver enterprise-grade capabilities including dynamic knowledge integration, improved factual accuracy, and reduced computational overhead. These systems enable organizations to maintain current information without the prohibitive costs associated with frequent model retraining.The significance for technical leadership lies in RAG’s ability to transform how enterprises approach knowledge management, decision support, and automated reasoning. By integrating RAG with Cloud-native architectures, organizations can build AI systems that scale e iciently while maintaining the accuracy and reliability required for mission-critical applications. [1] [2] [3]