The demo is easy and the product is hard: a chatbot that answers from a real, maintained knowledge base — prices, policies, opening hours — is a front-desk employee; one that improvises is a liability with a chat bubble. The difference is grounding, scope limits and a visible path to a human.
The questions that decide success are not which model but what it is allowed to do: read-only or able to act, which data it may quote, and what happens at the boundary — 'I do not know, here is how to reach us' is a feature, not a failure.
Related terms
RAG (retrieval-augmented generation)
Looking up relevant documents first and putting them in the prompt, so the model answers from your material instead of from memory.
Knowledge base
The maintained, structured store of facts a system answers from — documents, prices, policies — that a chatbot grounds on so it can only say what is true.
AI guardrails
The checks around a model that constrain what it can say and do — input filters, output validation, permissioned tools — so a bad response fails safely.
The bench this belongs to
AI automationThe repetitive half of your week, handed to software that does not get bored. Inbox triage, follow-ups, reporting, data entry between tools that were never meant to talk.
