Retrieval-Augmented Generation (RAG) Artificial Intelligence (AI) Framework
Search Enterprise AI, Thursday, October 5,2023
Retrieval-augmented generation (RAG) is an artificial intelligence (AI) framework that retrieves data from external sources of knowledge to improve the quality of responses. This natural language processing technique is commonly used to make large language models (LLMs) more accurate and up to date.
LLMs are AI models that power chatbots such as OpenAI's ChatGPT and Google Bard. LLMs can understand, summarize, generate and predict new content. However, they can still be inconsistent and fail at some knowledge-intensive tasks -- especially tasks that are outside their initial training data or those that require up-to-date information and transparency about how they make their decisions. When this happens, the LLM can return false information, also known as an AI hallucination.
By retrieving information from external sources when the LLM's trained data isn't enough, the quality of LLM responses improves. Retrieving information from an online source, for example, enables the LLM to access current information that it wasn't initially trained on.