"Retrieval Augmented Generation (RAG)" is a type of app that uses Large Language Models (LLMs) and Vector Search to provide more accurate and contextually appropriate answers to user queries by leveraging data stored in databases. This approach tackles the limitations of LLMs, such as token size limits, by selecting a proportion of relevant data from the database and passing it along with the query to the LLM. The Vector Search concept is used to efficiently find similar data using approximate nearest neighbor algorithms, allowing for fast and scalable search capabilities. Couchbase's Vector Search feature is utilized in RAG applications to perform efficient searches on large datasets. By leveraging RAG, developers can create more effective and accurate AI-powered applications that provide real-time results based on the most relevant data available.