◆ Google Looker

Troubleshoot Looker Studio data source issues

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 taskI need to fix the 'Invalid Dimension' error in the Q3 Sales Performance dashboard for Sarah by…+
I need to fix the 'Invalid Dimension' error in the Q3 Sales Performance dashboard for Sarah by end of day. It's pulling from the 'Sales_Data_Mart' source; check the 'Region' field mapping.
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 I present this Q3 dashboard to the executive team, make sure the sales data is robust.…+
Before I present this Q3 dashboard to the executive team, make sure the sales data is robust. Identify any data source issues that could cause a metric to misrepresent performance, especially for the 'New Customer Acquisition' chart.
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 Q4 forecast dashboard is showing completely flat sales numbers for Europe, which is…+
The Q4 forecast dashboard is showing completely flat sales numbers for Europe, which is impossible.
The Q4 forecast dashboard is showing completely flat sales numbers for Europe, which is impossible. The data source is 'Forecast_Model_v3', but I'm not seeing any errors in the data itself. The VP of Sales needs this by morning, and I'm worried it's a subtle data connection issue I'm missing, or perhaps a filter applied incorrectly somewhere. What's the most likely cause of this kind of data flattening, and what's the fastest way to narrow it down without breaking other reports?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI spend too much time chasing down data discrepancies reported by users after dashboards go…+
I spend too much time chasing down data discrepancies reported by users after dashboards go live.
I spend too much time chasing down data discrepancies reported by users after dashboards go live. It always feels like I'm reacting to problems rather than preventing them. What habit should I change to proactively catch data source issues before they impact the business users and their critical reports?
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.