Photo credit Retrieval Augmented Generation (RAG) may sound complex, but it accurately represents the process of the system. RAG is a method that enhances the capabilities of Large Language Models (LLMs) by integrating them with external knowledge sources. Each term represents a piece of the puzzle: Retrieval - data retrieved from some external source outside the LLM (most often a database, but can include files, webpages, etc) Augmented - "augmenting" (or adding to) an LLM’s training data.

Photo credit What is RAG? This term is used a lot in today’s technical landscape, but what does it actually mean? Here are a few definitions from various sources: "Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response." - Amazon Web Services "Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.