Retrieval Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by providing them with relevant and current information from external data sources, addressing limitations like stale or incomplete knowledge bases. RAG involves retrieving pertinent data from a vector database and a knowledge graph, which store information as numerical vectors and semantic graphs, respectively. This approach enables LLMs to generate more accurate and informative text by using real-time data without the need for time-consuming fine-tuning. By leveraging the strengths of both vector databases and knowledge graphs, RAG supports a wide range of applications, including summarization, question answering, and content creation. Guy Korland, an expert in database engineering and the CEO of FalkorDB, emphasizes the significance of using RAG to optimize LLM performance for tasks requiring up-to-date and comprehensive data.