Let AI help me find what to ask next

The menu handled headcount and budget — it never asked about the colleague with a peanut allergy. A complete answer can still only do half the job.

Tuesday morning, you need to order Friday lunch for 12 colleagues: budget ¥800, delivered to the meeting room at noon. AI quickly puts together four meat dishes, three vegetable dishes, and a staple — enough portions, within budget, utensil costs already set aside.

The plan looks ready to run. Then the boxes hit the table, and a colleague asks whether the sauce has peanuts. The restaurant confirms the dishes contain crushed peanuts, and the kitchen can’t guarantee no cross-contact. She has to wait for a last-minute replacement meal.

AI didn’t miscount the budget. It only finished the problem you already asked. What usually slips through is a condition you haven’t thought to ask yet. This time, do you stop at the menu — or let AI keep going until it finds the next question?

Alice

Next-question drill · Replies change with your choices

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It’s Tuesday morning. You need Friday lunch for 12 people, budget ¥800, delivered at noon. How would you usually start?

Using an ¥800 budget, put together a 12-person lunch menu with a mix of meat and vegetables. I found two restaurants with solid ratings. Compare price, portions, and delivery, then recommend one. Give me a menu first, then check that headcount, budget, and delivery time are all met.

Send All three moves are common. Pick the one you would actually use.

Next we add just one method: have AI find the next question for you.

Answer layerFirst get a plan that meets the known requirements.

Challenge layerFind conditions that would change the plan but haven’t been confirmed.

Next-question layerPick the highest-impact one, and write it as a question you can check with a person or the vendor right now.

You already have a menu. Now pick 3–5 items of information or actions below to help me decide what to ask next. A few look useful, but they’ll pull your attention off course.

Ask all 12 people one by one about allergies or restrictions Confirm ingredients and kitchen cross-contact with the restaurant Confirm the Friday noon delivery slot is still bookable Assign one person to collect replies and log anyone who hasn’t answered Send the candidate menu and restaurant to AI for another check Only pick the restaurant with the highest platform rating Order two extra hearty dishes — bigger portions feel safer Have AI make the menu richer and more professional

Send selection 0 selected, need at least 3 more

Try a different set

The move itself is short: first have AI stop recommending and check which unconfirmed conditions the current answer depends on; then rank them by impact, and rewrite only the most important one as the next question. That question needs someone to check with, something to check, and a pass bar.

In the lunch-order scene, AI should first turn “everyone can eat this” into a checkable question: confirm allergies and restrictions person by person, then confirm ingredients and kitchen handling with the restaurant. Only after that does the menu go from a fluent answer to a plan you can actually run.

The challenge layer is for seeing the gap. The next-question layer is for pushing a real-world action. Every time you get a new reply, you can have AI find the next question again — until the key conditions are confirmed, or you know for sure you still shouldn’t act.