What's the most confidently wrong answer AI has given you about your data?
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I'm not talking about the obviously ridiculous AI hallucinations. I'm talking about those answers that looked completely believable until someone actually checked the numbers.
Maybe it was a calculation, a misleading chart, or a metric that meant something entirely different from what the AI assumed. What happened, and how did you catch it?
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Mine was a translation that passed every glance I gave it.
I build a flashcard generator tool. A Spanish to English deck came back with "la penumbra → 半明 light, semi-darkness". The English part is right, and the card looked fine in a list of thirty. But two Chinese characters had slipped into a deck where neither language uses them.
I didn't catch it by reading. I caught it by checking every card's script against the two languages in the pair. It turned up in 2 of 57 decks, which is exactly the rate where nobody notices by eye.
The part I'd push back on is the instinct to clean it up. Stripping the stray characters leaves "light, semi-darkness", which is a different answer than the model gave. So we drop the card instead and tell the user they got fewer than they asked for.
@hamanovich That's such an interesting example! The meaning wasn't necessarily wrong, but the output still was. And it's exactly the kind of mistake that could easily slip past someone, especially in a list of 30 flashcards.
I also find it interesting that you caught it through validation rather than simply reviewing the answers. It really shows how important it is to have checks in place beyond just asking, "Does this look right?"
the scary ones arent the made up numbers, its when the answer is right for some slice of the data and gets presented like its the whole picture. looks fine, passes the sniff test. i think the fix is making the tool say what it didnt check, not just what it found. a clear couldnt verify this is way more useful than a confident answer. do you show that kind of uncertainty in querri or is it all on the user to catch
In SEO data the believable one is Search Console's average position. Ask an AI where a page ranks and it will happily say "position 8", but that number is averaged across every query the page showed up for, weighted by impressions. A page can sit at #3 for the query that matters and around #40 for fifty long-tail ones, and the average describes neither. Filtering by query before asking fixes most of it.
The other one is counting things that exist instead of things that work. A report says 200 backlinks were placed and every row looks fine. Across 34,382 placements we tracked, only 36.3% of the linking pages ended up indexed by Google, so "placed" and "counts" were very different numbers. It only showed up when we checked each URL instead of the total.