Retrieval-Augmented Generation, or RAG, is a technique designed to combat AI "hallucinations," which are inaccurate answers delivered with confidence. At its core, RAG allows an AI model to look up information before answering a question, rather than relying solely on data learned during training, which can become outdated and does not include a company’s internal documents or policies. A RAG system first searches for relevant information from approved sources and then uses that information to generate a response. According to a 2025 blog post published by Nvidia, Patrick Lewis, one of the lead authors on the paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," acknowledged the unfortunate acronym, saying he and the team "definitely would have put more thought into the name had we known our work would become so widespread." He explained it was a case of quickly naming the paper when it was set to be published. The biggest reason RAG is useful is that it is far more accurate than previous versions of the technology, and it is quickly becoming the preferred way for businesses to build AI systems.