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RAG (Retrieval Augmented Generation) — a simple and clear explanation

People keep asking me what RAG is (in the context of large language models), and I keep wanting to share a link to an article on habr that would explain the concept of RAG (Retrieval Augmented Generation) in simple terms yet in detail, including instructions on how to implement this "beast." But there is still no such article, so I decided not to wait and write one myself.If something in the article is unclear or there are aspects of RAG that need to be elaborated on, don't hesitate—leave comments; I'll break it down and add more if necessary. Okay, let's go…RAG (Retrieval Augmented Generation) is a way of working with large language models where the user writes their question, and you programmatically mix in additional information from some external sources to that question and feed it, as a whole, into the language model. In other words, you add additional information to the model's context, based on which the language model can give the user a more complete and accurate answer.The simplest example:The user asks the model: "What is the current dollar exchange rate?"

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  • llm   
  • Natural Language Processing   
  • rag