What is RAG?
Retrieval Augmented Generation (RAG) is an innovative architecture that enhances the capabilities of Large Language Models (LLMs) like ChatGPT. It integrates an information retrieval system into the generative process, providing a grounding layer of data that the LLM can use when formulating responses. This approach allows for more accurate and contextually relevant outputs by leveraging external, verified information sources. RAG operates by first retrieving pertinent information from a vast repository of knowledge and then using a generative model to formulate responses based on this data. The synergy between the retrieval and generative components enables RAG to produce high-quality, informative, and relevant answers, which is particularly useful in applications such as AI chatbots, question-answering systems, and content creation. By incorporating RAG, developers can create AI applications that are not only more responsive to user queries but also provide answers that are grounded in factual information, thereby reducing the spread of misinformation and bias often associated with purely generative models.