Zero-Shot Learning
00:04:18 · Nancy Wang
[00:04:18] S3: So given that the problem space, right, of many businesses is so vast... So for example, take 1Password. Right? For us, we're looking to build, for example, dev specific workflows. Another company might want to build customer support, or maybe even recruiting specific workflows. How do you even know what good looks like in that case?
00:04:35 · Adarsh Hiremath
[00:04:35] S2: Yeah, yeah. Even just taking the example of 1Password, right? The key thing is it's not just an agent that does dev workflows. It's an agent that does dev workflows workflows within one password. And for that agent to do a good job, it needs to know what one password considers is good for a specific role or a set of developer tasks. Right? And that's like the the the magic thing here. Right? Even for simple workflows, just the general intelligence of the model is not sufficient. Right? Let's just say reviewing a resume. You could take the smartest model and then have it review your resume. If it's not calibrated with your recruiting team and what that team's looking for in the talent profile of a resume, it's just gonna fall flat on its face. So the question of how do we understand what good looks like requires extracting the knowledge from an enterprise. It can be everything from connecting to SaaS tools, uploading documents, AI moderated interviews of people actually doing the job. And the second thing is an eval that helps us understand the failure modes. Specifically, the eval would be the task the model is trying to do, the trajectory or reasoning steps that that agent is taking, and then the actual outcome graded against a set of subjective or objective criteria that we call a rubric.
00:05:39 · Dev Tagare
[00:05:39] S1: Makes sense. Yeah. So so just double clicking into this a little bit, Azarsh. You know, there's always this thing of, like, expert judgment in any of the enterprise context. Right? Like, usually enterprises are deep in a specific domain area. And when you go a transition from quantitative data to somewhat expert opinion or expert judgment, more qualitative, more intuition driven, there is a lot of spectrum for signal versus noise in this journey. One, how do you solve for it? Two, when you really dig into this, are there any, like, failure patterns or modes that have surprised you more than than others?
00:06:18 · Adarsh Hiremath
[00:06:18] S2: Yeah. So, addressing the point about signal to noise, I would say that that is very, very use case specific. Right? So for example, in customer support, there's really only two metrics you care a lot about. Right? One is, did the AI resolve the ticket end to end? So that's ticket resolution rate. And two is, did it have a satisfaction for the customer at a rate better than a human or on par with a human? Those are two things that are very, very easy to hill climb, with the right tools, and it's a quantitative thing as opposed to a qualitative one. There are other use cases where the value judgments are just entirely, you know, vibes based. Right? And that's like a really, really important thing to get right. Yeah. For example, if you're trying to automate a consulting workflow at producing a slide deck, right, it's not, clear cut or not, like, support resolution. Mhmm. Right? So then the question becomes, how do you actually structure the preferences of the person who knows what goods looks like for that specific task in a rubric format? Right? And there, can actually break it apart into the discrete things that you would look for if you're a human evaluating the slide deck. For example, an objective thing might be whether or not the slide deck sticks to specific color formatting. Right? But a subjective thing might be whether or not the actual content on the slide deck is not verbose, easy to understand, follow some typical structure. Mhmm. Right? So the key thing is combining both of those four tasks that are unbounded and subjective, in this format.
00:07:38 · Dev Tagare
[00:07:38] S1: And are are there, like... In your journey, have you noticed some metrics that may look great on paper, but then when you go to production, when you start seeing real world use cases, they don't look as good. Perhaps one thing I've noticed over time is search as a problem space in AI land, where you get a lot of these answers really quickly, think AI mode or think whatever your favorite AI application is to get answers. Everyone personalizes to some degree here and there, but then over time, your preference is kind of distinct from mine, kind of distinct from Nancy's. Yeah. It wasn't a problem ever in SearchLand because you would just get a link back. Like, you you would get the 10 little links and it was rank rank preference across many people. But now, are you noticing that the user preference is dominating even enterprise outcomes, and gradations there?
00:08:29 · Adarsh Hiremath, Dev Tagare
[00:08:29] S2: Yeah. So, I mean, one thing that's really important is understanding the inputs into an output or or the reasoning, that actually leads to a specific, output of the model. Right? Because you could have a model that just answers correctly the first time, but it's making all the wrong decisions along the way. And then when you adapt it to a slightly different context, all of a sudden, you've got an agent that's totally broken in production. Right. So that's why the evals need to not only be over the task that it's trying to do in the output, which is the rubric that I just described, but also the trajectory that connects the two. Because you could have an LLM judge or a human evaluating whether the reasoning is sound, and if that reasoning is sound and the output is achieved properly, odds are the overall deployment is gonna go well, assuming you have, like, a reasonable distribution of all three of those things, inputs, trajectories, and outputs. To your point about whether these output metrics can be deceiving at time, the answer is 100% yes. Right? [00:09:15] S1: Yeah. [00:09:16] S2: And it's no different from metrics for like a product team, for example. Right? Where if you really, really wanted to game a metric like most product teams could, so it's about having the right right guardrails. Mhmm. Going back to the example that I, you know, pointed out earlier about customer support, you could have a high resolution rate, but the way that the agent actually communicates with the human could be totally messed up. [00:09:36] S1: Right.