◆ Elasticsearch

Update multiple documents by query

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 update the 'status' field to 'archived' for all documents in the 'logs-2023-q4' index…+
I need to update the 'status' field to 'archived' for all documents in the 'logs-2023-q4' index where the 'timestamp' is older than '2024-01-01T00:00:00Z'. Make sure it only affects documents with 'type: old_data'.
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 run this update to archive old logs, check if any of the documents I'm targeting are…+
Before I run this update to archive old logs, check if any of the documents I'm targeting are still referenced by active dashboards or reports. If so, flag them so I don't break someone's current view.
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 tried to update a batch of user profiles and the operation timed out after 30 seconds,…+
I just ran a query to update a batch of user profiles and the operation timed out, but some documents were changed.
I just tried to update a batch of user profiles and the operation timed out after 30 seconds, but I see some documents were changed. The client is asking for confirmation. I'm afraid I've left the data in an inconsistent state. I don't know how to tell which documents were updated and which weren't, or if I should just re-run it. What's the best way to diagnose the partial update and ensure data integrity without causing more issues?
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 losing time trying to revert or clean up after bulk operations that fail midway through,…+
I keep losing time trying to revert or clean up after bulk operations that fail midway through.
I keep losing time trying to revert or clean up after bulk operations that fail midway through, leaving inconsistent data. This happens too often when the cluster is under load. What habit should I change to ensure my bulk operations are atomic, or at least easily recoverable, so I don't spend hours untangling partial updates and frustrating stakeholders?
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.