Retrieval-Augmented Generation (RAG)
RAG — Retrieval-Augmented Generation (RAG) is a technique where an AI model retrieves relevant documents from the live web or a database and uses them to ground its answer, instead of relying only on what it memorized during training.
RAG is why an assistant can name a business it was never explicitly trained on and cite a source for it. When you ask 'best dentist in Austin', a RAG-enabled assistant fetches current pages — directories, reviews, local lists — and composes its answer from them.
For businesses this is the mechanism you can influence. Training data is fixed and opaque, but the documents a model retrieves at answer time are the live web. Being present and well-described on the pages it retrieves is how you get pulled into a RAG answer.
Key points
- →The model fetches live sources, then writes the answer from them.
- →Explains citations and up-to-date, location-specific recommendations.
- →The retrieved pages are the surface you can actually optimize.
Related terms
See your own AI visibility
Run a free scan and see how ChatGPT, Claude, Gemini & Perplexity describe your business — with a plan to get named more often.
Run my free scan →