AI is confidently wrong way more than people give it credit for, change my mind

been using AI heavily for research and analysis work and the thing that keeps getting me is how confident it sounds even when it's wrong. not hallucinating fake facts necessarily, more like taking thin or ambiguous data and presenting a conclusion with the same tone as when it has strong data behind it. concrete example: i had it analyze a batch of customer feedback and rank the top complaints. it gave me a clean list, no hedging, no "this is uncertain." went back and checked the raw source myself and one of the "top complaints" showed up twice out of like 200 comments. two. but it was presented with the exact same confidence as the complaint that showed up 60 times. no flag, no "low sample size," nothing. just a tidy ranked list that looked equally solid all the way down. i think the issue is these models are optimized to sound coherent, not to communicate uncertainty. a human analyst who only has 2 data points for a claim will usually say "not sure this one's real, small sample" because admitting uncertainty is normal human behavior. the model doesn't do that unless you explicitly force it to, because generating a hedge isn't rewarded the same way generating a clean answer is. what worries me is how easy it is to not notice. the output reads so professionally that you stop questioning it. i only caught the fake pattern because i happened to spot check the raw data, if i hadn't, that 2-out-of-200 complaint would've ended up in an actual strategy doc as a "top concern." this is a known limitation people have found workarounds for or if we're all just supposed to manually verify everything forever, which kind of defeats the point of using AI to save time in the first place change my mind, is this actually a big deal or am i overthinking a fixable prompting problem