[nums]

Maybe a difference here is asking AI for conclusions. When I have it do a buyer's report for me, I ask it for "what questions should I be asking? What are typical things that go wrong with this type of vehicle?" I don't delegate conclusions to the AI but use it to educate myself. Then, I can gather further information to make MY decision .. to buy it or not.

[rockostrich]

I don't think so. LLMs tend to over-index on providing results in general whether it's a conclusion or not. When you ask it "What are typical things that go wrong with this type of vehicle?" you're forcing it to make a conclusion about which results to include and it will almost certainly provide results even if those issues aren't as much of a concern compared to typical issues with other vehicles.

For example, I just prompted Kimi-K2.6 with:

> I'm considering buying a used base model 2010 Honda Civic with 80k miles that's been garage kept. What are typical things that go wrong with this type of vehicle?

It listed 10 issues including the engine block cracking (which wasn't even an issue with 2010 Civics). Started a new chat and asked about a 2010 Toyota Camry, another unbelievably reliable car, and it listed 9 similar issues. Started a new chat and asked about a 2011 Jeep Grand Cherokee, a notoriously unreliable vehicle, and it listed the same number of issues.

Sure it's data to make decisions on either way, but it really all comes down to how good your prompts are and whether or not you can think critically about the output, whether or not that output is a conclusion or just data collection.