Retrieval-Augmented Generation (RAG) is transforming enterprise search by moving beyond traditional keyword-based systems. Instead of relying on boolean queries that often yield stale or irrelevant results, RAG combines semantic retrieval with large language models (LLMs) to generate context-aware responses. This approach directly addresses issues like vocabulary mismatches and outdated information, providing users with more relevant and conversational answers to their queries. The article outlines key aspects of implementing RAG within an organization. It explains the architecture behind RAG, detailing components like embedding models and vector stores. A comparative analysis highlights the differences between traditional search methods and RAG-powered semantic search, emphasizing performance metrics and cost considerations. Security and compliance are also addressed, with guidelines for managing sensitive data and maintaining system audits. Implementation considerations are crucial for successful integration. The article mentions data ingestion strategies, chunking methods, and scaling vector databases as essential steps. It also discusses integration patterns with existing search solutions and presents business use cases in sectors like retail, where RAG could enhance customer experiences through chatbots and conversational shopping. By focusing on practical applications and potential pitfalls, the article provides a roadmap for organizations looking to adopt RAG for improved search capabilities.