Jesse Zhang
speaker
452 appearances
1 recordings
1 series
first heard Jul 2026
last heard 31 Jul
Jesse Zhang’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.
Appearances
I think a common misconception that people have is fine-tuning is a way to customize it for that customer.
In fact, most of the fine-tuning we do is customizing it for our use case, like the customer service use case.
And it's worth it for us to do it because that's all we do.
We do these agents across
all of these different customers and so it is worth it for us to put in a ton of time and research into like how do you tune this one model to be good at selecting customer service topics but if you're the enterprise is it really worth your valuable research resources to like tune a model for these like customer service behaviors probably not right so that that's like that's one reason why people partner with applications another reason is
let's say I do put in the engineering effort to build like some agent myself using the frontier models.
And, you know, to my earlier point, you know, I'm not fine tuning for behavior, but I'm sort of teaching the AI my own procedures.
And again, that doesn't happen through fine tuning.
That happens like in context, because if you were to fine tune on that, you would have to reverse it every single time you change your procedures, which doesn't make sense.
And so you kind of build out your logic and you're building your business logic in.
Well, then you launch the agent.
And then the second day, you look at your conversations and you're like, oh, well, actually, I need to change these three things.
And now it's more engineering effort to do that.
And it's like constantly engineering effort.
So I think people will partner with applications when the use case calls for a broader platform where there's a lot of value in using, you know, the stuff that we've fine tuned and using the software stack we've built on top of the models to capture business logic.
And that stuff has nothing to do with the models, right?
Like how the business logic gets
captured by this AI?
How do you handle someone calling in because their flight was canceled and they need to rebook three people at once?
So that is business logic that the AI needs to know, and you're encoding that, but that has nothing to do with the models themselves.
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