What is Retrieval-Augmented Generation (RAG)?

RAG is an architectural pattern in generative AI designed to enhance the accuracy and relevance of responses generated by Large Language Models (LLMs). It works by retrieving external data from a vector database at the time a prompt is issued. This approach helps prevent hallucinations, which are inaccuracies or fabrications that LLMs might produce when they lack sufficient context or information.

To ensure that the data retrieved is always current, the vector database should be continuously updated with real-time information. This ongoing update process ensures that RAG pulls in the most recent and contextually relevant data available.