满意度 4.6 分
满意度 4.6 分，8000 份问卷。你先问什么？
Copy the prompt, then pick a model. It plays the interviewer and asks you through it scene by scene instead of handing you the answer.
On a phone with the app installed, the system opens the app directly. Links cannot open external apps inside WeChat — open it in a browser first.
The copied text ends with the answer key the model grades you against. Don't read it yourself first — working it out on your own is the point.
这一份追的第一件事是：谁答的。 8000 份听着不少，回收率 2%；而最不满意的那批人早就走了，或者根本懒得答 —— 主观打分天然是「还愿意跟你说话的人」的分数。
然后它给你一张分布表：72% 给了 5 分，9% 给了 1 分。平均值 4.6 把这 9% 抹平了，而那 9% 是这份数据里唯一值钱的部分。
第三幕问那个不舒服的问题：只在用户体验最好的那一刻弹问卷，算作弊吗？判据是什么？ 多数公司把这件事叫优化。
What this one tests
This prompt in the repo (the answer key is at the end of that file — you'll see it)
Have you done this one?
Sign in to mark this one as practised — next time you come back you'll see when you did it.
Notes from people who did this one
How to use this
There is no model answer here — but there are definite wrong answers. Only three things make a case hold up, and every prompt's answer key carries a "common wrong answers" section that is more useful than the checklist itself.
The three things that make it hold up
- You can say whose problem this is and what it is — **with evidence** (not "I think users would want it")
- You can say what success looks like — **and what that number is today** (no baseline, no acceptance criteria)
- You can name the cost and what you gave up — **the hardest one to fake**, which is why every prompt hunts for it
How to start
- Pick a question, open it, copy the prompt.
- Paste it into a brand-new conversation — not one where you already discussed it.
- Say nothing. Wait for its first question.
Four commands you can call out at any time
跳过move to the next scene now提示give a direction, not the answer直说吧spell this point out in full, then keep pressing on the trade-offs复盘jump to the wrap-up and print the review card
Three traps
- Don't rush to a solution. More than half the lost points here come from designing before asking — especially describing features before defining what success means.
- Expect to be pushed for numbers. "It'll perform better", "users will like it" get cut off: which metric, what is it today, and when do you look?
- When it starts praising you, point it back to rule five — feeling validated crowds out noticing what you missed.
Start with the north star metric; it underpins everything else. Then pick by what's on your plate: a backlog you can't order → prioritisation; nobody using it or retention sliding → retention and onboarding; need to price something → pricing. Save the experiment review for later, and keep redoing the one about turning a one-line request into a spec — it's the closest to daily work.
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