Explained
What is RAG (retrieval-augmented generation)?
RAG (retrieval-augmented generation) is a technique where an AI model first looks up the answer in a defined knowledge base and only then writes its reply. If the answer isn't in there, the model invents nothing and says it doesn't know. That makes RAG the standard way to let a chatbot talk reliably about one business.
How RAG works, in plain terms
A language model without RAG answers from its general training — so it guesses when it doesn't know your rates or service area. With RAG a step comes before the writing: the question is first fired at a knowledge base as a search query (documents, rates, opening hours, terms), and only the retrieved passages go to the model as context. The model may only write based on what was found. That makes every answer traceable to a source you supplied.
Why this is the difference between daring and not daring
The reason many businesses don't dare put a chatbot on their site is exactly the guessing: one invented price or made-up warranty term costs more trust than a hundred good answers earn. A RAG chatbot structurally doesn't have that problem: if something isn't in the knowledge base, it says so and hands the conversation to a human. Our FAQ chatbot works this way, and the voicebot shares the same knowledge base — so an answer on the phone and in the chat never drift apart.
What RAG does not solve
RAG is not a magic wand: the answers are as good as the knowledge base. If your rate changes and nobody updates the knowledge base, the bot neatly quotes the old amount — source citation and all. That's why a RAG chatbot comes with a management process: every month we review which questions came in, where the bot had to pass, and extend the knowledge base accordingly. That monthly upkeep is included in our rent, because a knowledge base that stands still becomes a liability instead of a salesperson within six months.
Where this shows up on the site
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