◆ Elasticsearch

Show all aggregation results/buckets

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 taskShow all unique values for the 'country' field in the 'user_data' index. I need to see all the…+
Show all unique values for the 'country' field in the 'user_data' index. I need to see all the different countries present.
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 the quarterly business review, generate a report for the 'sales_transactions' index. I…+
Before the quarterly business review, generate a report for the 'sales_transactions' index. I need to see the total revenue broken down by 'product_category' AND 'region'. Make sure all categories and regions are included, even if some have low counts, so the finance director gets the full picture.
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 trying to show the distribution of 'error_codes' from the 'server_logs' index for the last…+
My aggregation is cutting off results, and I'm losing important data for a critical report.
I'm trying to show the distribution of 'error_codes' from the 'server_logs' index for the last 24 hours, but the report only shows the top 10 codes, and I know there are more. The operations manager needs to see ALL unique error codes to identify emerging issues. I'm afraid of misrepresenting the system's health. How do I ensure my aggregation shows every single unique error code and its count, no matter how infrequent?
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 run into situations where my initial aggregations only show a partial view, and I…+
I often find myself manually adjusting aggregation sizes because initial queries miss important details.
I constantly run into situations where my initial aggregations only show a partial view, and I have to go back and manually increase sizes or adjust parameters to capture all the data points the business needs. This reactive approach wastes time and can lead to incomplete insights. What habit should I change in my approach to designing aggregations to ensure I capture all relevant buckets from the start?
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 Elasticsearch'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.