Definition
Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is a technique that feeds an AI model your own current documents at the moment of a question, so its answer is grounded in real data instead of only what it memorized during training.
A Large language model (LLM) knows only what was in its training data: generic, and frozen in time. RAG bolts a retrieval step onto that. When a question comes in, the system first searches a trusted store of your own content (your menu, hours, allergen notes, policies, past reviews), pulls the most relevant snippets, and hands them to the model along with the question. The model then answers from those snippets rather than from memory or a guess.
For a restaurant this is the difference between an AI that sounds right and one that is right about YOUR business. A generic model does not know your gluten-free options or that you just 86 (to 86 an item)'d the special, but a phone agent (Voice AI) or website chat connected to your live menu and reservation book can answer "do you have a vegan entree tonight" accurately. RAG is also the main defense against Hallucination: by forcing the answer to come from retrieved facts, and letting the tool cite them, it keeps the model honest.
You almost never build RAG yourself; it is plumbing inside good tools. What matters as an operator is that the tool is wired to a current, accurate source of your data. A phone agent whose menu snapshot is a month stale will confidently quote a dish you no longer serve, which is worse than no agent at all. The question to ask any AI vendor is simple: where does its answer come from, and how fresh is it. See AI phone answering.
See it in action
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