◆ Google Looker

Develop LookML models

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 taskAdd a new 'Customer Lifetime Value' measure to the 'Customer Analytics' LookML model, defined…+
Add a new 'Customer Lifetime Value' measure to the 'Customer Analytics' LookML model, defined as total revenue per customer, and deploy it to production by end of day Friday.
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 the 'Customer Analytics' model to the marketing team, can you review the new…+
Before I present the 'Customer Analytics' model to the marketing team, can you review the new 'Customer Lifetime Value' measure? Ensure it's accurately calculated, clearly described for business users, and performs efficiently without slowing down queries. Flag any potential ambiguities or performance bottlenecks.
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've developed the new 'Customer Lifetime Value' measure in LookML, but when I test it, the…+
I've built the new 'Customer Lifetime Value' measure, but the numbers don't look right, and I can't figure out why.
I've developed the new 'Customer Lifetime Value' measure in LookML, but when I test it, the values are much higher than expected, and I can't pinpoint the error. The marketing lead needs this for a campaign launch next week. I'm worried I've misjoined something or made a subtle aggregation mistake. How do I effectively debug this and ensure the calculation is sound?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI often spend too much time debugging LookML models after they're supposedly complete, leading…+
I frequently struggle with ensuring my new LookML measures are accurate and performant before deployment.
I often spend too much time debugging LookML models after they're supposedly complete, leading to delays and rework. What habit should I change to more effectively validate the accuracy and performance of new measures and dimensions during development, so I can deploy with confidence and avoid last-minute fixes?
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.