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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
See the prompt↓All Elasticsearch tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskArchive last month's logs from the production cluster to cold storage. Make sure the indices…+
Archive last month's logs from the production cluster to cold storage. Make sure the indices are marked read-only before the move.
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 we archive the old logs, review the retention policy for the 'billing-service' index.…+
Before we archive the old logs, review the retention policy for the 'billing-service' index. We've had issues retrieving specific records for compliance audits after they're archived, so ensure the necessary fields are still easily searchable in cold storage.
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 billing team needs a transaction record from six months ago that should be in cold storage,…+
The billing team just asked for a specific transaction from six months ago, and I can't find it in the usual archive. I'm worried we miscategorized it or deleted it too soon.
The billing team needs a transaction record from six months ago that should be in cold storage, but I can't locate it through the standard archive search. I'm afraid we might have used the wrong retention tag or that the data was somehow corrupted during the last archive run. What's the most likely place it could be, and what's the fastest way to check without disrupting the production cluster?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternWe're constantly struggling to quickly retrieve specific historical data from our archives for…+
We keep losing time and credibility when we have to retrieve specific historical data from archives for audits or customer inquiries.
We're constantly struggling to quickly retrieve specific historical data from our archives for audits or customer support, leading to delays and frustration. Our current archiving process often makes specific records hard to pinpoint later. What habit should I change in how we tag or structure our archives to ensure critical data is always easily retrievable when needed?
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.