Shreya Rajpal discusses why AI simulation is needed in production to catch unexpected model regressions.
Insight
Rajpal: Foundation models rarely generate toxic outputs without explicit jailbreaks
“Most of the stuff that the frameworks will recommend is actually stuff that the model providers are already working on. So toxicity, unless you're doing, unless somebody is very explicitly trying to jailbreak what you've built, you know, you won't run into the…”
Insight
Rajpal: AI simulations should prioritize product KPIs over generic safety metrics
“So I would actually say that like a lot of the things to simulate are more aligned with like product KPIs or product metrics that actually make Whatever AI system you're building very sticky, rather than, you know, focusing more on, like, traditional safety se…”
Insight
Rajpal: Fine-tuning open-source models on synthetic data closes proprietary capability gaps
“Not out of the box, but with a lot of that fine tuning and that the training, et cetera, you are able to kind of close the gap and even have better performance on metrics.”
Insight
Rajpal: AI agents resemble autonomous vehicle architectures with cascading ML units
“What patterns really worked well in self-driving cars, which is weirdly a very similar system to, you know, agents of today where you have like these cascading kind of like units that are all machine learning based and, you know, they all kind of like feed int…”
Insight
Rajpal: Simulations reveal which failure modes actually require runtime guardrails
“And then, you know, in simulation, figure out, you know, what is actually robust, what isn't, and then the stuff that isn't robust is the stuff that you need guardrails for.”
Insight
Rajpal: Product managers already act as AI persona engineers
“Interestingly, there are already persona engineers, and we call them like product managers, basically, you know. So your product managers are already thinking about, okay, I've built this, you know, model or this chatbot or this agent. Who are the personas? Wh…”