Manufacturing a gold standard eval dataset before launch

I'm honestly spinning my wheels on a pre-launch project and hitting a wall with the evaluation strategy. The standard advice is to build your dataset from production logs but since we have zero users that is a complete non-starter. I need a baseline and a gold standard before we go live but it feels like I'm basically manufacturing the ground truth out of thin air. Right now I'm spending way too much time building out synthetic users and adversarial personas basically trying to brute-force every way a human might try to break the prompt or trigger a hallucination. It feels like a lot of heavy lifting for something that might not even reflect real usage once we open the doors. I really want to avoid over-engineering a fake dataset that becomes irrelevant on day one but I also do not want to do a massive restructure later. I'm using Braintrust to manage the versions and evals just to keep a single source of truth but I'm still trying to figure out the best way to structure this now so the transition to real traces isnt a mess. For anyone who has launched recently how did you handle the day zero dataset? Did you go full synthetic or did you just ship it and fix it in production?