What is RAG?

Retrieval-Augmented Generation (RAG) is an AI framework that combines the power of pre-trained large language models (LLMs) with the ability to access and incorporate information from external knowledge sources during the generation process. Instead of relying solely on the knowledge embedded in the LLM's parameters, RAG models first retrieve relevant information from a knowledge base (e.g., a document repository, a database, or the web), and then use this retrieved information to inform the generation of text. This allows the LLM to generate more accurate, up-to-date, and contextually relevant responses, particularly when dealing with information that the model was not trained on or that has changed since its training.