RAG

Retrieval-augmented generation (RAG) is a model that combines a retrieval component with a generative model. This means that when an RAG model is prompted to generate text or answer a question, it first retrieves relevant information from a vast database. It then uses this information to provide responses that are created by using specific, real-world data rather than relying solely on pretrained knowledge.

This dynamic approach allows RAG models to produce more accurate, timely, and contextually appropriate outputs, significantly reducing the occurrence of errors and hallucinations that are typical of traditional models.