Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a method in natural language processing that enhances the generation of text by dynamically retrieving and incorporating relevant information from a large database or document collection during the generation process. This approach combines the capabilities of information retrieval systems and language models, aiming to produce more accurate, informative, and contextually rich outputs. RAG models achieve this by first retrieving documents related to the input query and then using these documents to guide the generation of the final text response.