Perhaps "conceptualization" is the indicator here.
Also I don't know what you mean by "conceptualization".
I mean "being able to conceive an idea". As humans, two or more of us can reason our way to a conclusion without domain knowledge. There is an upper limit where the idea is incomplete (assuming respectful ignorance), but it's generative nonetheless.
With an LLM I have to prompt engineer to guide it. I would rather have it generate novel concepts to push domain boundaries. They work great as knowledge bases though.
That sounds like step-by-step thinking?
> With an LLM I have to prompt engineer to guide it.
I generally have to in humans, too. I mean, you and I are prompting each other, aren't we?
For me the difference between prompting a human and prompting an AI is that I can reset the AI, I can't make a human forget a previous analogy that had only confused them. (And likewise, I don't expect that I fully forget bad analogies which confuse me, though I do try).
> They work great as knowledge bases though.
IMO, that's their weakest part. We had knowledge bases before — where each claim can be easily localised within the model, corrected when it needs to be, verified in advance, and which give predictable output — LLMs are none of those things.
LLMs are much better at understanding the question (constant time for a fixed-length output, even when the query is phrased badly and relatively complex), and being able to synthesise things in the form of "${x} won't work, try ${y}".
I use them as a cooperative partner by default.
Also: quite a few people have had instances work with other instances, sometimes of the same model and sometimes of other models.