Retrieval Augmented Generation (RAG) is a technique used to mitigate hallucinations in large language models by leveraging external knowledge sources. It combines the capabilities of information retrieval with advanced generative capabilities of language models to provide detailed, contextually accurate answers. RAG involves three critical components: Retrieval, Augmentation, and Generation. The Retrieval component fetches relevant information from an external knowledge base, while the Augmentation component enhances and adds more context to the retrieved response. The Generation component uses a large language model (LLM) to craft precise responses based on the provided context. RAG allows models to pull in real-time information from external sources, making them dynamic and adaptable to new information, particularly useful for tasks like fact-checking or answering questions about recent events. By integrating SingleStoreDB with RAG, organizations can harness the power of real-time analytics and fast data retrieval, ensuring timely and relevant responses to user queries.