I'm quite aware that it's less likely to technically hallucinate in these cases. But focusing on that technicality doesn't serve users well.

These (interesting and useful) use cases do not address the core issue that the query was written by the LLM, without expert oversight, which still leads to situations that are effectively halucinations.

Technically, it is returning a "correct" direct answer to a question that no rational actor would ever have asked.

But when a halucinated (correct looking but deeply flawed) query is sent to the system of record, it's most honest to still call the results a halucination, as well. Even though they are technically real data, just astonishingly poorly chosen real data.

The meaningless, correct-looking and wrong result for the end user is still just going to be called a halucination, by common folks.

For common usage, it's important not to promise end users that these scenarios are free of halucination.

You and I understand that technically, they're not getting back a halucination, just an answer to a bad question.

But for the end user to understand how to use the tool safely, they still need to know that a meaningless correct looking and wrong answer is still possible (and today, still also likely).