AI models carry the hidden choices of whoever built them, which is why the same prompt gets different answers. Training a custom model from scratch using Hugging Face is possible but demands serious time and computing power.
Who's crazy? And I didn't give it any other guidance. So knowing what these, like, presumptions the models are making, I think is really important, because those presumptions are the data scientists who built those models. That's their feature engineering that you're seeing when you see varied answers, right, in a model where you ask the exact same question. So knowing that, I think, is key. But, like, I start with big models, and then my goal is to understand what are my options when it comes to a small model. You have a couple. One, you could build it yourself, right? You could take a a model from Hugging Face and you could train it on a bunch of data to create your own dedicated small language model, but that's pretty expensive both in time and in
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