Sarah Sachs: belief

Transcript context

…Yeah. Yeah. I mean, one thing that, one phrase we say internally in lot is to, to teach at the top of the class. You know, we wanna build like, like the customization’s, kind of like a power tool. I mean, we try to make it as easy as possible to set up, but we want it to be pretty deep and sophisticated. And I think a huge part of that is the operator needs to be able to interrogate. The way the system works. And a big part of that is like, what are the tools? How do they work? You know, like, like how should I prompt it to use the tools in the right way? I’d actually say we don’t try and make it as easy as possible to use. ‘cause the more we do that, the more we abstract away that interpretability, that Simon’s talking about, that basically nerfs the model or nerfs the agent from being super capable. So a huge. I would say turning point, I can think about like the week and a half that we all came together on this as we were building custom agents, was that alignment that we’re not trying to build for everyone here. We’re not trying to build the model that, um, or build the user experience that anyone can figure out how to use. ‘cause the more we do that, the more we just diminish its capabilities. And that was a big, you know, everyone in a couple Slack messages aligned on that, that actually made us all work faster again. Right? ‘cause we all were like more centralized on who we were building for Alsesio: what does the meta prom generator look like? So I looked in the system prompt that it, gen, for example, uses emojis. That’s not a, you know, obvious thing to be doing. Wait, did you just Alsesio: ask it? What’s your system prompt? Oh no. This is how to generate prompts. The Alsesio: prompts generate prompts.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.