Retrieval-augmented generation (RAG) is an innovative method used to enhance large language models (LLMs) by linking them to external data sources, thereby improving the accuracy and relevance of their outputs. This approach addresses the challenges of LLMs, such as hallucinations and outdated knowledge, by incorporating real-time data and domain-specific information, effectively turning the model into an "open-book" system. RAG's integration into enterprise AI applications across various sectors, including finance, healthcare, public services, energy, and manufacturing, has demonstrated its potential to optimize processes, enhance decision-making, and ensure the reliability of AI-generated information. Despite the considerable resource investment required for implementation and the need for ongoing maintenance and security measures, RAG is poised to become a staple in AI development due to its ability to deliver contextual understanding and source attribution. RAG as a Service (RaaS) further simplifies integration by offering managed solutions that allow businesses to leverage the benefits of RAG without the need for extensive infrastructure.