An LLM is a lossy encyclopedia

A lossy encyclopaedia should be missing information and be obvious about it, not making it up without your knowledge and changing the answer every time.

When you have a lossy piece of media, such as a compressed sound or image file, you can always see the resemblance to the original and note the degradation as it happens. You never have a clear JPEG of a lamp, compress it, and get a clear image of the Milky Way, then reopen the image and get a clear image of a pile of dirt.

Furthermore, an encyclopaedia is something you can reference and learn from without a goal, it allows you to peruse information you have no concept of. Not so with LLMs, which you have to query to get an answer.

> You never have a clear JPEG of a lamp, compress it, and get a clear image of the Milky Way, then reopen the image and get a clear image of a pile of dirt.

Oh but it's much worse than that: because most LLMs aren't deterministic in the way they operate [1], you can get a pristine image of a different pile of dirt every single time you ask.

[1] there are models where if you have the "model + prompt + seed" you're at least guaranteed to get the same output every single time. FWIW I use LLMs but I cannot integrate them in anything I produce when what they output ain't deterministic.