Simon Willison’s Weblog

Since I love collecting questionable analogies for LLMs, here's a new one I just came up with: an LLM is a lossy encyclopedia. They have a huge array of facts compressed into them but that compression is lossy (see also Ted Chiang).

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.

This thought sparked by a comment on Hacker News asking why an LLM couldn't "Create a boilerplate Zephyr project skeleton, for Pi Pico with st7789 spi display drivers configured". That's more of a lossless encyclopedia question!

My answer:

The way to solve this particular problem is to make a correct example available to it. Don't expect it to just know extremely specific facts like that - instead, treat it as a tool that can act on facts presented to it.