What is Retrieval Augmented Generation (RAG)? 

Retrieval-Augmented Generation (RAG) is an advanced natural language processing (NLP) and artificial intelligence (AI) model architecture that combines the capabilities of both information retrieval and generation models. It's designed to enhance the performance of language models by allowing them to access external knowledge sources in real time.

In a traditional language model, the system generates responses based solely on the data it was trained on, which is static and limited to the training phase. RAG overcomes this limitation by retrieving relevant information from large external datasets (like a database, search engine, or knowledge base) during the generation process. This means that the model can pull up-to-date and contextually relevant information from outside its training data, making its outputs more accurate and comprehensive.

Learn more about RAG Pros and Cons here.