This Week's Term: Retrieval-Augmented Generation (RAG) - an AI architecture that combines large language models with dynamic information retrieval, allowing models to fetch relevant documents or data before generating responses, improving accuracy and enabling up-to-date answers without retraining.
RAG is the technical foundation behind many "talk to your data" solutions, including NotebookLM. Instead of relying solely on what's in the model's training data, RAG systems first search your documents or databases for relevant information, then use that retrieved context to generate responses. This approach solves two major LLM problems: outdated information and hallucinations. For business leaders, understanding RAG helps explain why these systems can cite sources and stay current without constant retraining - the retrieval step is doing the heavy lifting of finding the right information.
If you want to get a general overview of what RAG is and how is it relevant to business use cases, Matthew Berman does a good job of explaining it in the video below: