>> Okay, awesome. So, we talked a bunch about like what worked for us while we were building evals. Of course, your mileage may vary depending on your application. Things can uh things can differ. Uh some of the things that we wanted to recap here was uh it's like what what we think makes a good eval system generally is like it should be representative of what you want your product to be great at. And that will differ depending on the state at which your agent is when you're building MVP cases, it would look differently versus when you're doing production rollout. So that would differ. Uh but it still needs to be very much uh centered around what do you want your product to be good at and optimizing for that. Uh important to of course keep it evolving. That's why we talked about having your online eval having test sets that are refreshed with production data, having sampling pipelines, all sorts of things. Uh highly curated golden sets which will also evolve as your use cases evolve. So training teams whether it's scale raers or your cross functional teams on how to rate things how what are you expecting out of them that's also very important that's uh I think now it's getting more mainstream so hopefully it's less less controversial but like six months ago our teams were like still figuring out okay how do we like do this what's expected out of it so I think investing in those trainings can be helpful uh and then raider templates and rubrics with clear set of examples so you don't have scale raers coming back to you saying I'm not sure how to rate this. Um lots of like things getting marked as like unknowns or I don't know things like that. Also choosing the right launch metrics. So some of the launch metrics launch slides that we showed you. This is a very high level of generally how you would do a launch readiness. You would like check it. You'll do bunch of iterations on the model. You'll do an AB diff or ablation and you'll try to see okay where is the regression happening? What's an acceptable regression versus not? Things like that. As you're doing these systems, it's important to like uh get some clarity early on on what is your gatekeeping rule like what's your launch criteria. Is there a certain precision recall number that you're looking at? Is there some other metric that you look at? If you're doing a model eval, then probably that metric looks different than just the usual precision recall. So those things can also be important to keep in mind. Uh yeah, those are all the tips that we have to build production grade eval. Thank you.