Comment Re:Artificial Scarcity (Score 1) 48
My experience thus far with the various LLM chatbots is that they've all been trained with the default idea that users want to one-shot solutions.
A human in a similar situation normally would ask questions to clarify the problem, and attempt to ascertain the level of domain knowledge so that they can intelligently communicate.
Part of the song and dance in forming a prompt is to pre-load the model with all those items, so that you don't burn an obscene amount of tokens for it to finally get to the point where it can be useful. An open ended query can be useful for discovery but only if you're willing to do the legwork to cross-reference against other models and reference material.
To put it another way... most humans are lazy and just want to put in the minimum of work, and are willing to accept a 60% acceptable answer from a bot (basically using it as a search engine replacement). Add the fact that most humans are not domain experts, nor are they able or willing to educate themselves to a minimum standard, they have no way of judging whether the bot answer is even at the 60% standard - instead they do what most people do... if it sounds good, and the bot claims to be an "expert", then they go with that.
I anticipate that models specifically curated for specific domains will become more and more useful. I've seen models plugged into residential and commercial electrical code, for example, to act as an always available code compliance helper for a specific municipality. And, as you point out, as long as you can look up the relevant section of code to verify that the LLM supplied advice is correct, and do the load calculations to make sure the math is correct, you can validate the LLM's answer.