The article argues that AI design teams should stop treating every model change like a product launch and instead build tighter test loops, clearer success metrics, and a healthy skepticism about demo magic.
The article opens with a visual framing of how to think about AI without losing your footing.
Hari
“Tuning by vibes” is painfully real — when you say “tighter test loops, ” are you talking about something like a fixed eval set you run on every model change (even tiny prompt tweaks), or more of an ad-hoc checklist the team revisits as the product shifts? I might be wrong here.
I read “tighter loops” as a small fixed eval set you can run every time, even for tiny prompt changes, because otherwise you’re just re-litigating taste each week. The ad‑hoc checklist still matters, but I’d treat it like a periodic design review thing, not the thing that blocks every merge.
Look — a small fixed eval set you can run on every change is the only way to catch “oops we regressed” before it ships. Just make sure it includes at least a couple adversarial cases (prompt injection / data exfil style) so you’re not only measuring vibes.
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