That’s where retrieval-augmented generation (RAG) comes in. RAG provides a way to optimize the output of an LLM with targeted information without modifying the underlying model itself; that targeted information can be more up-to-date than the LLM as well as specific to a particular organization and industry. That means the generative AI system can provide more contextually appropriate answers to prompts as well as base those answers on extremely current data. RAG first came to the attention of generative AI developers after the publication of “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” a 2020 paper published by Patrick Lewis and a team at Facebook AI Research. The RAG concept has been embraced by many academic and industry researchers, who see it as a way to significantly improve the value of generative AI systems.