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 user will at least need to know something about the topic beforehand.

This is why I've said a few times here on HN and elsewhere, if you're using an LLM you need to think of yourself as an architect guiding a Junior to Mid Level developer. Juniors can do amazing things, they can also goof up hard. What's really funny is you can make them audit their own code in a new context window, and give you a detailed answer as to why that code is awful.

I use it mostly on personal projects especially since I can prototype quickly as needed.