What is Retrieval Augmented Generation (RAG)?
Retrieval Augmented Generation (RAG) is an artificial intelligence technique that combines information retrieval with text generation. It enhances language models by allowing them to access and incorporate external knowledge sources during the generation process. In a RAG system, a retrieval component first fetches relevant information from a large corpus of data, and then a generation component uses this retrieved information along with the input query to produce more accurate, informed, and up-to-date responses. This approach bridges the gap between static knowledge embedded in pre-trained models and dynamic, current information available in external databases or documents.
Example use cases
What is RAG used for?