Retrieval-Augmented Generation (RAG) is an advanced model architecture that combines retrieval systems with generative models to produce more accurate and factual outputs by leveraging external knowledge. RAG integrates two key components: a retriever that fetches relevant information from a knowledge base and a generator, such as GPT, that uses this retrieved information to generate responses. This approach helps to mitigate the issue of generative models creating factually incorrect information by grounding responses in real-world data. RAG can retrieve data from various sources, including Wikipedia articles, documentation, news, and custom datasets, making it particularly useful for domain-specific tasks like legal text generation and medical Q&A. The model employs hybrid retrieval methods, blending dense and sparse retrieval techniques to capture both exact matches and semantically similar documents. RAG's applications span customer support, legal advice, and scientific literature search, among others. Additionally, RAG models often outperform pure language models on tasks requiring factual accuracy, such as open-domain question answering. RAG uses early fusion and late fusion strategies to combine retrieved data with generated text. New advancements like GraphRAG, MemoRAG, and OneGen further enhance the capabilities of RAG.