An LLM is a lossy encyclopedia

I totally agree with the author. Sadly, I feel like that's not what the majority of LLM users tend to view LLMs. And it's definitely not what AI companies marketing.

> The key thing is to develop an intuition for questions it can usefully answer vs questions that are at a level of detail where the lossiness matters

the problem is that in order to develop an intuition for questions that LLMs can answer, the user will at least need to know something about the topic beforehand. I believe that this lack of initial understanding of the user input is what can lead to taking LLM output as factual. If one side of the exchange knows nothing about the subject, the other side can use jargon and even present random facts or lossy facts which can almost guarantee to impress the other side.

> The way to solve this particular problem is to make a correct example available to it.

My question is how much effort would it take to make a correct example available for the LLM before it can output quality and useful data? If the effort I put in is more than what I would get in return, then I feel like it's best to write and reason it myself.

> The key thing is to develop an intuition for questions it can usefully answer vs questions that are at a level of detail where the lossiness matters

It's also useful to have an intuition for what things an LLM is liable to get wrong/hallucinate, one of which is questions where the question itself suggests one or more obvious answers (which may or may not be correct), which the LLM may well then hallucinate, and sound reasonable, if it doesn't "know".

LLMs are very sensitive to leading questions. A small hint of that the expected answer looks like will tend to produce exactly that answer.