Retrieval-Augmented Generation (RAG) is an approach that enhances the accuracy of large language models (LLMs) by allowing them to access external, up-to-date information, thereby reducing the inaccuracies often associated with LLM-generated responses. Developed by researchers from FAIR, UCL, and NYU, RAG integrates LLM capabilities with additional data sources, such as a company's knowledge base, to provide more precise and contextually pertinent answers. Unlike semantic search, which relies solely on pre-trained data, RAG combines retrieval and generation techniques to incorporate trusted external sources, making it suitable for various applications including Q&A systems, conversational systems, educational tools, and content generation. Implementing RAG involves selecting a pre-trained language model, using document retrieval techniques, contextual embedding, and potentially fine-tuning the model for specific applications. This methodology not only enhances the quality and relevance of responses but also allows for domain-specific customization, resulting in a more conversational and user-friendly interaction.