For example, I’m not a domain expert, but I was looking for an RC motor for a toy project and OpenAI had happily tried to source a few, with Deep Research. Only the best candidate it had picked contained an obvious typo in the motor spec (68 grams instead of 680 grams), which is just impossible for a motor of specified dimensions.
It's funny you say that because I was going to echo your parents sentiment and point out it's exactly the same with any news article you read.
The majority if content these LLMs are consuming is not from domain experts.
That's not really true, since your prompts are also getting better. Better input leads to better output remains true, even with LLMs (when you see it as a tool).
That said, the “AI all the things” mandates could be the lever that ultimately accomplishes what 100+ PjMs couldn’t - making people write issues as if they really mattered. Because garbage in, garbage out.
However in this specific example, I don't remember if it was chatgpt or gemini or 3.5 Haiku but the other(s) explained it well enough. I think I re-asked 3.5 Haiku at a later point of time, and to my complete non-surprise, it gave an answer that was quite decent.
1 - For example, the field of DIY audio - which was funnily enough the source of my question. I'm no speaker designer, but combining creativity with engineering basics/rules of thumb seems to be something LLms struggle with terribly. Ask them to design a speaker and they come up with the most vanilla, tired, textbook design - despite several existing market products that are already so much ahead/innovative.
I'm confident that if you asked an LLM an identical question for which there is more discourse - eg make an interesting/innovative phone - you'd get relatively much better results.
I am a novice, maybe that's why I liked it.
