◆ Google Looker

Pull all data into one place

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 taskPull all customer support ticket data from Zendesk for the last 90 days into a new…+
Pull all customer support ticket data from Zendesk for the last 90 days into a new dataset.
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 acceptWhen I pull all our customer interaction data from Zendesk and Salesforce, make sure it flags…+
When I pull all our customer interaction data from Zendesk and Salesforce, make sure it flags customers who have opened more than three support tickets in the last month and haven't made a purchase in the same period. I need to identify our most frustrated, at-risk customers.
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 product team is demanding a comprehensive view of feature usage across all user segments,…+
The product team needs to understand feature usage across all user segments, but the data is scattered across three different databases.
The product team is demanding a comprehensive view of feature usage across all user segments, but the relevant data lives in our product database, marketing automation platform, and CRM. I'm struggling to get a unified view without losing critical context from each source. I'm afraid of delivering an incomplete picture that leads to bad product decisions. What's the best strategy to pull all this disparate data into one place reliably and ensure the metrics are consistent across systems?
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 spending hours manually joining data from our CRM, marketing platform, and…+
I spend too much time manually joining data from different sources for every new analysis request.
I'm constantly spending hours manually joining data from our CRM, marketing platform, and product database for new analysis requests. This process is slow, error-prone, and prevents me from focusing on deeper insights. What habit should I change to create a more integrated data foundation so I can quickly access a unified view of our customers and operations?
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.