RAG means Retrieval-Augmented Generation. In plain English, it is a way to let an AI answer questions using documents you provide. The app first retrieves relevant information, then the LLM generates an answer using that retrieved context.
An LLM alone has three big limitations:
- It can hallucinate. If it does not know something, it may still produce a confident answer.
- It has a knowledge cutoff. It cannot know every update that happened after training.
- It cannot see your private data unless you provide it in the prompt or connect it to a tool.
RAG solves the information-access problem. The core idea is simple: at query time, find the best evidence and place it in the model's context window.
A real-world analogy: imagine a lawyer answering a question. Without RAG, the lawyer answers from memory. With RAG, the lawyer first searches the case files, pulls the relevant paragraphs, reads them, and then answers with references.
RAG is used today in customer support bots, legal research assistants, medical literature search, internal company knowledge bases, sales enablement, product documentation assistants, enterprise search, coding assistants, financial research tools, and compliance workflows.