People vs AI processing

There are some fundamental differences between how most people think and how AI LLMs work.

In most conversations, and especially in narrative writing, people start out with a particular focus or context, but as the conversation progresses, the focus often narrows down from that larger focus. But that change of contextual scope is not something that needs to be explicitly mentioned, except perhaps for people with autism that can miss the context change because there are no obvious cues. Thus people can talk and the focus can narrow, broaden or pivot to something related, even conceptually by analogy or metaphor.

Conversely, LLMs are adding to the same context as the interaction proceeds, so some answers will include information that is no longer relevant to a person who has narrowed their focus. This can be frustrating for people to have to tell the LLM to ignore some aspect that it brought up.

But LLMs do progressively weigh more recent discussions as more relevant, degrading earlier ones, perhaps by paring them to salient points. This can also cause frustration for people who expect the larger context that was part of earlier discussions in the session to still be relevant and be able to be referred to at will.

These two LLM patterns are intrinsic to their mechanical operation, so they cannot be changed by programming, tagging or special prompting. This may help to understand the real limitations of LLMs, besides that they do not actually know whether the information they have is actually true, but may just be statistically consistent in their training data to be perceived as probably being true. Armed with this understanding, better but realistic use of LLMs can be had.

Computers raise the lower bar in any endeavour, making it easier to produce a better standard of results, and so a lot of what are called bullshit jobs can easily be done by LLMs. But it takes human creativity to raise the upper bar, providing new ways to perceive and take action. But we do not need to wait for other people or LLMs to do that for us, as we can use our own creativity and our hands to build something novel that someday may be implemented by LLMs for all.