Why Asking AI "What Would You Ask Me?" Gets You Better Results

How to let AI interview you for better results

Twenty minutes into trying to get AI help with visualizing your customer retention findings, you're still stuck with generic chart suggestions.

• You've explained your executive audience twice.

• You've clarified that you need something more compelling than a basic table three times.

• You keep adding context about the story you want to tell, but the responses stay focused on standard chart types.

Here's what finally worked for me: I stopped trying to guess what AI needed and asked it directly.

"What would you ask me to suggest the best way to visualize these findings?"

The response was a list of six specific questions, some of which I hadn't thought to address:

• "What's the most counterintuitive finding in your data that might surprise viewers?"

• "Are there any data limitations or caveats that could affect interpretation?"

• "What action do you want viewers to take after seeing this visualization?"

• "Have you tried showing this data before, and if so, what didn't work?"

• "What's the biggest risk if viewers misinterpret this data?"

• "Are there competing explanations for these trends that you should address?"

I answered them in one message and got exactly the targeted visualization approach I needed.

The suggestion?

Create a "before and after" comparison chart showing retention rates in the six months before our onboarding changes versus after, with callout boxes highlighting the specific program improvements that drove each uptick.

Without those diagnostic questions, AI would have suggested a standard retention trend line that missed the intervention story entirely.

That simple flip - from guessing to asking - has cut down on my circular AI conversations.

Why We Keep Playing Twenty Questions

Most of us approach AI like we're ordering at a restaurant where the server can't ask clarifying questions. We try to anticipate every detail upfront or watch our request get misunderstood.

However, we often don’t realize that AI knows what information would help. It just waits for you to provide it instead of asking for it.

When you request help with "visualizing trends," AI thinks about dozens of possible approaches.

Line charts? Heat maps? Small multiples? Monthly or quarterly views? Focused on patterns or outliers?

Without context, it defaults to generic visualization advice that covers all possibilities instead of specific guidance for your situation.

The Pattern Everyone's Discovering

So what's really happening when you flip the question? AI stops guessing and shifts into diagnostic mode.

I've watched this same realization happen across dozens of data professionals. They start asking AI what questions it would ask, and suddenly their conversations become efficient.

The technique works because it shifts AI from guessing your needs to diagnosing your situation.

Instead of forcing AI to interpret incomplete information, you're letting it interview you like a consultant would.

Basic version: "What would you ask me to help visualize this data effectively?"

Specific versions:

• "What details would help you suggest the right chart type?"

• "What would you need to know to recommend the best visualization approach?"

• "What questions should I answer to get targeted design feedback?"

Real Applications That Work

Data Visualization Design:

❌ Before: "Create a chart showing our quarterly results."

✅ After: "I need to visualize our quarterly performance data. What would you ask me to recommend the right chart approach?"

Possible AI's response:

• "What's the most unexpected finding in this data?"

• "Are there any data quality issues that could mislead viewers?"

• "What's the biggest misconception people have about this topic?"

• "What action should viewers take after seeing this?"

• "Have you shown similar data before, and what questions did people ask?"

Chart Improvement Feedback:

❌ Before: "What's wrong with this chart?"

✅ After: "I created a performance trend chart but it's not communicating clearly. What would you ask to understand what's not working?"

Possible AI's response:

• "What's confusing viewers about this chart?"

• "What's the intended insight versus what people are actually taking away?"

• "Have you tested this with anyone?"

• "What specific misunderstandings have come up?"

• "What constraints do you have for redesigning this?"

Presentation Visuals for Stakeholders:

❌ Before: "Help me create charts for a stakeholder presentation."

✅ After: "I need to create compelling visuals for our quarterly stakeholder meeting. What would you ask to design charts stakeholders can grasp in a 3-minute review?"

Possible AI's response:

• "What's the biggest concern stakeholders have about this topic right now?"

• "What misconceptions might they bring to the data?"

• "Are there political sensitivities around these findings?"

• "What's happened when similar data was shared before?"

• "What's the follow-up conversation you're trying to enable?"

So what's the payoff beyond better charts?

The Three Things This Actually Changes

Immediate Problem-Solving:
You get relevant answers faster because AI has the context it needs upfront.

Conversation Efficiency:
You spend less time explaining and re-explaining your situation.

Diagnostic Skill Development:
You start learning how to think through problems systematically by watching AI's interview process.

The third benefit builds over time.

After asking "What would you ask me?" regularly, you start anticipating what information AI needs. Your first prompts become more complete, and circular conversations become rare.

When to Use This (And When Not To)

Best for:

• Complex analysis where context matters

• Technical troubleshooting with multiple variables

• Creative projects where your vision needs translation

• Situations where you're not sure what information is relevant

Skip it for:

• Quick factual lookups

• Simple calculations

• When you're confident your request is already complete

Advanced Applications

Once you're comfortable with basic reverse questioning, you can get more specific:

Priority Filtering:

"If you could only ask me three questions to prioritize this analysis, what would they be?"

Assumption Testing:

"What assumptions about my data or business context should I clarify?"

Scope Clarification:

"What boundaries should I set for this project that would affect your recommendations?"

These variations help you surface the specific factors that matter most for your situation.

What Happens When It Doesn't Work

Sometimes the diagnostic questions feel obvious or irrelevant. Here's how to troubleshoot:

If questions seem generic:
Your original request was probably clear enough. Answer what feels relevant and continue.

If AI asks too many questions:
Pick the 3-4 most important ones first. You can always address others later.

If questions miss the mark:
AI misunderstood your goal. Clarify your objective and try again. You may also need to reset your context.

If you need a quick answer:
This technique works best when you have time for a slightly longer setup in exchange for much better results.

The Lasting Benefit

Over time, this does more than fix AI conversations - it changes how you approach problem-solving with people too.

Learning to ask "What would you ask me?" develops skills that extend far beyond AI conversations.

When you get comfortable letting AI interview you about projects, you naturally become better at defining requirements, understanding stakeholder needs, and communicating constraints clearly.

These diagnostic thinking patterns start showing up in your work with human colleagues too.

• You ask better questions in project meetings.

• You provide more complete context in emails.

• You anticipate what information others need.

It's not just about AI fluency - it's about becoming someone who thinks clearly about problems and communicates effectively about solutions.

Your Next Conversation

Pick one frustrating visualization challenge from this week - maybe a chart that didn't communicate clearly, or trying to present complex data in a simple way.

Before jumping into your next similar conversation, start with:

"What would you ask me to understand this [visualization challenge] better?"

Notice which diagnostic questions surprise you. Use those insights not just for this conversation, but as a checklist for future requests.

The goal isn't perfect AI conversations. It's efficient ones.

What did you think of this issue? Are you ready to try it?

Chat soon,
Donabel