◆ Google Looker

Use custom fields in Looker Explores

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 taskI need to see the average order value for our enterprise clients in the last quarter. Can you…+
I need to see the average order value for our enterprise clients in the last quarter. Can you create a custom field in this Explore that calculates that, excluding any orders under $500?
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 share this Explore with Sarah in Sales, help me make sure the custom fields are easy…+
Before I share this Explore with Sarah in Sales, help me make sure the custom fields are easy for her to understand. Can you rename 'Calc_Avg_Order_Val_Ent' to 'Enterprise AOV (Excl. <$500)' and add a description explaining the exclusion criteria?
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 built a custom field for 'First Purchase Channel' but it's showing nulls for 30% of our…+
I just built a custom field for 'First Purchase Channel' but it's showing nulls for 30% of our new customers.
I just built a custom field for 'First Purchase Channel' but it's showing nulls for 30% of our new customers. My manager, David, needs to see this data by end of day for the board meeting, and I'm worried about presenting incomplete information. I can't ask him for more time. Is this a data quality issue upstream or an error in my custom field logic? What's the fastest way to diagnose and fix this before 5 PM?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI keep spending too much time debugging custom fields that break or show unexpected results,…+
I keep spending too much time debugging custom fields that break or show unexpected results.
I keep spending too much time debugging custom fields that break or show unexpected results, especially when underlying data models change. This eats into my capacity for deeper analysis and makes me hesitant to build complex calculations. What habit should I change to build more robust custom fields that require less post-launch firefighting?
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.