RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation — instead of relying only on its training memory, the model first retrieves relevant documents from a knowledge base and answers using them. This slashes hallucinations and lets AI answer from information it was never trained on: your product docs, this week's news, your private notes. It is how chat-with-your-PDF tools and grounded enterprise assistants work under the hood. For creators, RAG-style grounding matters whenever accuracy counts — like generating product descriptions from a real spec sheet instead of trusting the model's imagination to get the details right.