◆ Google Looker

Clean data 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 taskClean the customer feedback survey data from SurveyMonkey, removing duplicate entries and…+
Clean the customer feedback survey data from SurveyMonkey, removing duplicate entries and standardizing text responses, so it's ready for sentiment analysis by tomorrow morning.
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 hand off this customer feedback data to the product team, make it easy for them to…+
Before I hand off this customer feedback data to the product team, make it easy for them to spot critical issues. Surface the top 5 recurring complaints, highlight any high-severity bug reports, and flag responses that suggest a major usability problem with the new feature.
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'm seeing a lot of inconsistent spelling and phrasing for key product features in the…+
I'm seeing a lot of inconsistent spelling and phrasing for key product features in the open-text survey responses.
I'm seeing a lot of inconsistent spelling and phrasing for key product features in the open-text survey responses, like 'checkout' vs 'check out' vs 'payment'. This makes it impossible to accurately group feedback. I'm afraid my analysis will be flawed and mislead the product team. What's the most efficient way to standardize these terms without losing nuance, and what's my best next move to ensure accuracy by Friday's deadline?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI constantly find myself re-cleaning similar data issues across different datasets, like…+
I constantly find myself re-cleaning similar data issues across different datasets.
I constantly find myself re-cleaning similar data issues across different datasets, like inconsistent date formats or misspelled company names, which wastes valuable time. I need a more proactive approach. What habit should I change to embed better data quality practices earlier in the data pipeline, reducing the need for repetitive manual cleaning?
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.