Grounding

A grounded answer carries its evidence: this claim, from that passage, in that document, at that version. The value is not decoration for the customer, who mostly will not click it. It is that the deployment becomes debuggable and defensible — when a customer says they were told something, the transcript says what was retrieved and what was said, and the two can be compared by somebody who was not there.

Asking the model to explain its reasoning is not the same thing and is not a substitute. A model will produce a plausible account of how it reached an answer whether or not that is how it reached it, and the account is generated by the same process that generated the answer. Grounding is a property of the pipeline: the passages are recorded before the answer exists.

For anything with a price, a deadline or an eligibility rule in it, this stops being a nice property. In the UAE a statement made to a consumer about price or terms is not a conversational flourish, and “our chatbot said it” has no standing anywhere. What the system says is what the business said.

Showing sources without checking the answer is in them

Citations rendered under an answer look like grounding and often are not: the passages are the ones retrieved, and whether the claim actually appears in them is a separate question nobody checks. Sample a few dozen answers and read the cited passage. An answer whose source does not contain it is worse than an unsourced answer, because it has borrowed the appearance of verification.

Somebody will quote this number at you.

Knowing the definition is not the same as being able to check the figure. These are the procedures that do the second thing.

Accepting an automated agent

“The agent is trained on your data and ready to go live.” · 45 minutes, 6 questions.

RAG

Look the answer up first, then have the model phrase it — an architecture that turns a question about the model into a question about your material.

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Hallucination

A fluent, confident claim with nothing behind it — and the reason it is dangerous is that it is indistinguishable from a correct answer at the moment it is read.

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Knowledge base

The material the system answers from. It is the product; the model is a commodity you rent by the token.

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Adversarial test set

The questions written specifically to make the system invent an answer — and the only acceptance evidence that means anything.

All seventy-eight termsWhat this is regulated by in the UAE

Is this one right in your own numbers?

The definitions are the easy part. Whether the figure on your dashboard was computed this way is a different question, and usually the more expensive one.