> yoUr'E PRoMPTiNg IT WRoNg!
> Am I though?”
Yes. You’re complaining that Gemini “shits the bed”, despite using 2.5 Flash (not Pro), without search or reasoning.
It’s a fact that some models are smarter than others. This is a task that requires reasoning so the article is hard to take seriously when the author uses a model optimised for speed (not intelligence), and doesn’t even turn reasoning on (nor suggest they’re even aware of it being a feature).
I asked the exact prompt to ChatGPT 5 Thinking and got an excellent answer with cited sources, all of which appears to be accurate.
I think the authors point stands.
EDIT: I tried it with "Deep Research" too. Here it doesn't invent either TLDs or HTML Element, but the resulting list is incomplete.
There is also the question of the two input lists: it's not clear if it is better to ask the LLM to extract the two input lists directly, or again to ask the LLM to write a script that extract the two input lists from the raw text data.
Maybe they will be in a time frame when the LLM model is still in use.
Search and reasoning use up more context, leading to context rot, and subtler harder to detect hallucinations. Reasoning doesn’t always focus on evaluating the quality of evidence, just “problem solving” from some root set of axioms found in search.
I’ve had this happen in Claude code for example where it hallucinated a few details about a library based on what badly written forum post.
Isn’t that the whole goddamn rub? You don’t _know_ if they’re accurate.
Or, if LLMs are so smart, why doesn't it say "Hmmm, would you like to use a different model for this?"
Either way, disappointing.
That is indeed an area where LLMs don't shine.
That is, not only are they trained to always respond with an answer, they have no ability to accurately tell how confident they are in that answer. So you can't just filter out low confidence answers.
I’m presuming that one class of junk/low quality output is when the model doesn’t have high probability next tokens and works with whatever poor options it has.
Maybe low probability tokens that cross some threshold could have a visual treatment to give feedback the same way word processors give feedback in a spelling or grammatical error.
But maybe I’m making a mistake thinking that token probability is related to the accuracy of output?
Isn't that what logprobs is?
> Or, if LLMs are so smart, why doesn't it say "Hmmm, would you like to use a different model for this?"
That's literally what ChatGPT did for me[0], which is consistent from what they shared at the last keynote (quick-low reasoning answer per default first, with reasoning/search only if explicitly prompted or as a follow-up). It did miss one match tough, as it somehow didn't parse the `<search>` element from the MDN docs.
[0]: https://chatgpt.com/share/68cffb5c-fd14-8005-b175-ab77d1bf58...