Jerry Liu: evaluation

Transcript context

…I have a few hot takes. One is like RAG is basically just, just a hack, but it turns out it's a very good hack because what is RAG rag is you keep the model fixed and you just figure out a good way to like stuff stuff into the prompt of the language model and everything that we're doing nowadays in terms of like stuffing stuff into the prompt is just algorithmic. We're just figuring out nice algorithms to, to like retrieve right information with top case similarity, do some sort of like, uh, you know, hybrid search, some sort of like a chain of thought decomp and then just like stuff stuff into a prompt. So it's all like algorithmic and it's more like just software engineering to try to make the most out of these like existing APIs. The reason I say it's a hack is just like from a pure like optimization standpoint. If you think about this from like the machine learning lens, unless the software engineering lens, there's pieces in here that are going to be like suboptimal, right? Like, like the thing about machine learning is when you optimize like some system that can be optimized within machine learning, like the set of parameters, you're really like changing like the entire system's weights to try to optimize the subjective function. And if you just cobble a bunch of stuff together, you can't really optimize the pieces are inefficient, right? And so like a retrieval interface, like doing top cam batting lookup, that part is inefficient. If you, for instance, because there might be potentially a better, more learned retrieval algorithm, that's better. If you know, you do stuff like some sort of, I know nowadays there's this concept of how do you do like short-term and long-term memory represent stuff in some sort of vector embedding, do trunk sizes, all that stuff. It's all just like decisions that you make that aren't really optimized and it's not really automatically learned. It's more just things that you set beforehand to actually feed into the system. So I do think like there is a lot of room to actually optimize the performance of an entire LLM system, potentially in a more like machine learning based way. Right. And I will leave room for that. And this is also why I think like in the long term, I do think fine tuning will probably have like greater importance. And just like there will probably be new architectures invented that where you can actually kind of like include a lot of this under the black box, as opposed to having like hobbling together a bunch of components outside the black box. That said, just very practically given the current state of things, like even if I said RAG is a hack, it's a very good hack and it's also very easy to use. Right. And so just like for kind of like the AI engineer persona, which to be fair is kind 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. 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.…

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