Retrieval-Augmented Generation (RAG) represents an innovative approach in the field of large language models (LLMs) by allowing these models to access external databases for real-time, accurate information retrieval, similar to a student using resources during an open-book exam. This advancement addresses traditional LLM limitations related to outdated or incorrect data. RAG can be implemented in various forms, such as Naive RAG, which uses straightforward retrieval processes; Advanced RAG, which optimizes retrieval accuracy through sophisticated query transformations; and Modular RAG, which provides a flexible architecture for complex interactions. A new fine-tuning methodology called RAFT (Retrieval-Augmented Fine Tuning) is introduced to enhance domain-specific tasks by training models to focus on relevant information while ignoring distractors. The document further discusses Command R+, a RAG-optimized model, which utilizes a structured prompt template to enhance retrieval and response generation. Evaluation of RAG systems involves metrics that assess both retrieval accuracy and generation quality. As RAG technology evolves, it is poised to expand into multimodal and Knowledge Graph-based applications, enhancing AI systems' capabilities and applicability across diverse fields.