Jerry Liu: prediction

Transcript context

…Is that something that you'd be interested in supporting? I mean, I think in the longterm, like if like, this is kind of how fine tuning, like RAG evolves. Like I do think there'll be some aspect where fine tuning will probably memorize some high level concepts of knowledge, but then like RAG will just be there to supplement like aspects of that, that aren't work that don't, that, that it doesn't know. Um, the way I think about this is kind of like, obviously RAG is the default way, like to be clear, RAG right now is the default way to actually augment stuff with knowledge. I think it's just an open question of how much the LM can actually internalize both high level concepts, but also details as you can like train stuff over it. And coming from an ML background, there is a certain beauty and just baking everything into some training process of a language model. Like if you just take raw chat, GPT or chat, GPT code interpreter, right? Like GPT four, it's not like you do RAG with it. You just ask it questions about like, Hey, how do I like to find a pedantic model in Python? And I'm like, can you give me an example? Can you visualize a graph? It just does it right. Like, and we'll run it through code interpreters as a tool, but that's not like a source for knowledge. It's just an execution environment. And so there is some beauty in just like having the model itself, like just, you know, instead of you kind of defining the algorithm for what the data structure should look like the model just learns it under the hood. That said, I think the reason it's not a thing right now is just like, no one knows how to do it. It probably costs too much money. And then also like the API interfaces and just like the actual ability to kind of evaluate and improve on performance, like isn't known to most people. Yeah.…

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