◆ Google Looker

Export data from Looker

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 taskExport the raw data from the 'Customer Churn Analysis' report for the last quarter and save it…+
Export the raw data from the 'Customer Churn Analysis' report for the last quarter and save it as a CSV file named 'Q3_Churn_Data.csv' to the shared 'Analytics Exports' folder.
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 export this customer segment data for the product team, filter out any rows where the…+
Before I export this customer segment data for the product team, filter out any rows where the 'Customer Status' is 'Inactive' or 'Trial'. They only need current, paying customers for their analysis.
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 tried to export a massive dataset for the data science team, and it timed out. They need…+
I just tried to export a large dataset for the data science team, and it timed out, but they need this data by end of day for a critical model update.
I just tried to export a massive dataset for the data science team, and it timed out. They need this data by end of day for a critical model update, and I'm afraid of delaying their work. I can't tell them I'm stuck. Is there a common limit I'm hitting, or a better way to export very large datasets without crashing? What's the most efficient way to get them this data quickly?
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 exporting data for various teams, but I often run into issues: files are too…+
I frequently get requests for data exports that are either too large, contain sensitive information not meant for the requester, or are formatted incorrectly for their needs.
I'm constantly exporting data for various teams, but I often run into issues: files are too big, they contain sensitive columns not meant for the recipient, or the format isn't what they expected. This leads to rework and security concerns. What habit should I change in how I confirm requirements and prepare data for export to ensure it's always appropriate and ready on the first try?
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.