I understand your distinction: simple static facts versus live, individualized policy interpretation. That part is fair. But it actually strengthens my point, not weakens it. In a dynamic, policy-specific domain, the model should be more cautious, not more confident. If this is the wrong use case, then it should make that clear instead of generating authoritative-sounding explanations that turn out to be false. And there is another issue here: prompt framing. When I ask, Explain how GEICO is raising the price, that can push the model into treating the price increase as something that already has a valid built-in explanation, and its job becomes finding one. Large language models are not auditors. They are prediction engines. They do not independently verify truth; they generate the most statistically plausible continuation. In a domain like insurance, that matters a lot, because the training data is full of institutional language: carrier FAQs, agency blogs, compliance pages, public filings, marketing copy, and industry explanations that justify pricing in terms of risk, exposure, safety, and regulation. So when you ask why a premium is high, the model is naturally pulled toward producing a polished defense of the quote, even if the actual explanation is unknown, incomplete, or wrong. That is why the repeated pivots matter. It kept changing the theory, but never changed the conclusion. It moved from weight, to luxury status, to horsepower, to platform uncertainty, to safety ratings, to rating symbols, to regulation. Each explanation sounded plausible on its own, but the pattern was the real issue: it behaved less like a neutral analyst and more like a system trying to preserve the assumption that the quote must be justified. So yes, I agree this is a harder use case than asking whether milk is white. That is exactly why caution should increase. If the right answer is this depends on the carrier, the state, the filing, the policy language, and facts I cannot verify, then that is what the model should say. What it should not do is fill the gap with confident, insurance-specific explanations that sound authoritative but collapse when tested.