Have you ever tried to explain large language models (LLMs) to friends and family, only to be met with confusion or skepticism? I know I have. Some people think LLMs are stupid toys, while others believe they’re all-knowing magical machines. But how do you explain the concept in a way that’s easy to understand?
I’ve tried using analogies like ‘really smart parrots’ or ‘outstanding encyclopedias,’ but they don’t always stick. Recently, I came up with a new approach that seems to work better. I ask my friends, ‘If I gave you the world’s knowledge in a book, would you know what to look for?’
The idea is that LLMs are only as good as the input they receive. Garbage in, garbage out. If you ask a poorly phrased question or provide incomplete information, you’re unlikely to get a useful response. It’s not that the LLM is stupid or incapable; it’s just that it’s only as good as the data it’s been trained on and the prompts it receives.
I’ve found that this analogy helps people understand that LLMs aren’t magic, but rather powerful tools that require thoughtful input to produce useful output. And who knows, maybe one day we’ll have LLMs that can understand what we’re looking for, even when we don’t know how to ask the right question.