◆ Google Looker

Avoid common mistakes in Looker Studio

This is real work, not a feature someone invented — it comes from real job ads and real questions people asked. Below are four ready AI prompts: get it done, make it easy for the next person to say yes to, work out the right move when you are stuck, and stop it coming back.

4prompts

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskAdd a new page to the 'Customer Churn Analysis' dashboard showing churn by subscription tier.…+
Add a new page to the 'Customer Churn Analysis' dashboard showing churn by subscription tier. Use the 'subscription_tier' dimension and a line chart for monthly trends.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
BImprove — make it easier to acceptBefore the board meeting, review the 'Monthly Financials' dashboard for potential…+
Before the board meeting, review the 'Monthly Financials' dashboard for potential misinterpretations. Check for any charts that might imply causation where there's only correlation, or metrics that could be misleading without proper context.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentThe sales team just pushed back on the conversion rates in the 'Sales Funnel Performance'…+
The sales team is questioning the conversion rates in the 'Sales Funnel Performance' dashboard, saying they don't match their CRM numbers.
The sales team just pushed back on the conversion rates in the 'Sales Funnel Performance' dashboard, saying they are too high compared to their CRM. I double-checked the calculations, but I'm worried there's a subtle data aggregation issue or a filter misapplication that's skewing the numbers. What's the most common mistake that causes discrepancies like this between a dashboard and source systems, and how do I quickly audit it to restore their trust?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI'm constantly fielding questions about data discrepancies or unusual numbers in my dashboards,…+
I frequently get questions about why dashboard numbers don't match other reports or seem off.
I'm constantly fielding questions about data discrepancies or unusual numbers in my dashboards, which erodes trust and takes time to resolve. I want to build dashboards that are always accurate and transparent. What habit should I change to proactively identify and prevent these common data interpretation mistakes before they become issues?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

honest answers, no sign-up

Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Google Looker's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.