◆ Google Looker

Troubleshoot Looker dashboard filter 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 taskFix the 'Region' filter on the 'Monthly Sales Performance' dashboard; it's currently showing…+
Fix the 'Region' filter on the 'Monthly Sales Performance' dashboard; it's currently showing all regions instead of filtering down to 'North America' as it should. Make sure it works for all viewers.
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 acceptThe 'Product Category' filter on the 'Inventory Turnover' dashboard isn't working right. Before…+
The 'Product Category' filter on the 'Inventory Turnover' dashboard isn't working right. Before I push it live, give me a quick check of what might be causing it to show irrelevant options or not filter correctly, so I don't get another complaint from operations.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentI just updated the 'Monthly Revenue' dashboard's underlying data source, and now the 'Date…+
The 'Date Range' filter on the executive dashboard is showing incorrect dates after I updated the underlying data source.
I just updated the 'Monthly Revenue' dashboard's underlying data source, and now the 'Date Range' filter is showing dates from 2005, not the last 12 months. The finance director needs this by tomorrow morning, and I'm afraid I've messed up the data connection. What's the most likely reason for this date discrepancy, and what's the quickest way to get the filter working correctly again?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternIt feels like every time I update a data source or make a small change to a dashboard, a filter…+
Dashboard filters frequently break or show incorrect options after data source updates or minor changes.
It feels like every time I update a data source or make a small change to a dashboard, a filter breaks or shows irrelevant options, leading to user complaints and rework. I'm spending too much time troubleshooting these. What habit should I change in how I manage dashboard filters to make them more robust against underlying data changes?
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.