A short presentation by Robert Ta arguing that evaluations (evals) exist to speed product improvement rather than as an end in themselves. The chunk uses a health-coach example (fitness plans, grocery help, user interactions) to illustrate "error analysis": inspect application data to find where behavior goes wrong, fix simple errors directly (e.g., wrong API/tool call), and reserve formal evals for harder-to-diagnose problems where iteration and measurement will add value.
Chapters
I need a health coach that gives me fitness plans, grocery stuff, help me make decisions with that use case in mind, what's immediate value? You're looking at your user interactions, where the health coach is going off track, or it's interrupting the user before they finished their thought. There can be any number of errors. It's always good to look into your specific application in the data and see where are things not working. There's a systematic way of doing that. We call it error analysis. And it's not complicated. The idea is that you need to focus on what is actually going wrong and then try to iterate on it. And then you decide, like, do you need an eval? So in the case of like calling the wrong tool in your API call, you provided the wrong tool and you're like, oh, that's a stupid. That's my mistake, that's my bad. You don't need an eval. I would say just fix it. And. But there you will come to like some other errors that you're like, I don't really know how to fix this right now. That's like a more telltale sign that an eval will provide you value. The whole point of evals is to make your product better. It's not about doing the eval. It's about is your product getting better as fast as possible.
