An LLM is a lossy encyclopedia

> 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.

I used ChatGPT 5 over the weekend to double check dosing guidelines for a specific medication. "Provide dosage guidelines for medication [insert here]"

It spit back dosing guidelines that were an order of magnitude wrong (suggested 100mcg instead of 1mg). When I saw 100mcg, I was suspicious and said "I don't think that's right" and it quickly corrected itself and provided the correct dosing guidelines.

These are the kind of innocent errors that can be dangerous if users trust it blindly.

The main challenge is LLMs aren't able to gauge confidence in its answers, so it can't adjust how confidently it communicates information back to you. It's like compressing a photo, and a photographer wrongly saying "here's the best quality image I have!" - do you trust the photographer at their word, or do you challenge him to find a better quality image?