Shawn Wang: uncertainty

Transcript context

…d of one of the reasons generative AI has gotten so big is because it's way more accessible for everybody to get into, as opposed to just like traditional machine learning, it tends to be good enough. Right. And if we can basically provide these existing techniques to help people really optimize how to use existing systems without having to really deeply understand machine learning, I still think that's a huge value add. And so there's very much like a UX and ease of use problem here, which is just like RAG is way easier to onboard and use. And that's probably like the primary reason why everyone should do RAG instead of fine tuning to begin with. If you think about like the 80-20 rule, like RAG very much fits within that and fine tuning doesn't really right now. And then I'm just kind of like leaving room for the future that, you know, like in the end, fine tuning can probably take over some of the aspects of like what RAG does. I don't know if this is mentioned in your explainability also allows for sourcing. And at the end of the day, like to increase trust that we have to source documents. Yeah. So, so I think what RAG does is it increases like transparency, visibility into the actual documents, right. That are getting fed into their context.…

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