Retrieval-Augmented Generation (RAG) is a technique for including contextual information from external sources in a large language model’s (LLM) prompt. In other terms, RAG is used to supplement an LLM’s answer by providing more information in the prompt; thus, removing the need to retrain or fine-tune a model. For the purposes of this post, we will implement RAG by using Chroma DB as a vector store with the Nobel Prize data set.
