Jerry Liu: evaluation

Transcript context

…There's a bunch of pieces to rag. Obviously it's not a, just an acronym. And you two recently, you think every AI engineer should build the front scratch at least once. Why is that? I think so. I'm actually kind of curious to hear your thoughts about this. Um, but this kind of relates to the initial like AI engineering posts that you put out and then also just like the role of an AI engineer and the skills that they're going to have to learn to truly succeed because there's an entire On one end, you have people that don't really, uh, like understand the fundamentals and just want to use this to like cobble something together to build something. And I think there is a beauty in that for what it's worth. Like, it's just one of those things. And Gen AI has made it so that you can just use these models in inference only mode, call something together, use it, power your app experiences, but on the other end, what we're increasingly seeing is that like more and more developers building with these apps start running into honestly, like pretty similar issues that like we'll play just a standard engineer building like a classifier model, which is just like accuracy problems, like, and hallucinations, basically just an accuracy problem, right? Like it's not giving you the right results. So what do you do? You have to iterate on the model itself. You have to figure out what parameters you tweak. You have to gain some intuition about this entire process. That workflow is pretty similar, honestly, like even if you're not training the model to just like tuning a ML model with like hyper parameters and learning like proper ML practices of like, okay, how do I have like define a good evaluation benchmark? How do I define like the right set of metrics to do to use, right? How do I actually iterate and improve the performance of this pipeline for production? What tools do I use?…

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