◆ Elasticsearch

Retrieve all document _ids efficiently

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 taskRetrieve all document _ids from the 'transactions_2024_q1' index. The finance team needs them…+
Retrieve all document _ids from the 'transactions_2024_q1' index. The finance team needs them for an audit by Monday.
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 retrieve all _ids from the 'transactions_2024_q1' index, make sure the process is…+
Before I retrieve all _ids from the 'transactions_2024_q1' index, make sure the process is efficient and won't put undue load on the cluster. The finance team needs this data, but we can't impact live transactions.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentRetrieving all _ids from the 'transactions_2024_q1' index is taking hours and causing…+
Retrieving all _ids from the large 'transactions_2024_q1' index is taking too long and impacting cluster performance.
Retrieving all _ids from the 'transactions_2024_q1' index is taking hours and causing performance warnings on the cluster. The finance team needs these for an audit by Monday, and I'm afraid of crashing production. I can't tell if it's the query method or the cluster's current load. What's the most efficient and least impactful way to retrieve these _ids quickly without affecting live operations?
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 and causing performance jitters on the cluster whenever I need to extract…+
Extracting large sets of _ids or specific fields from big indices always impacts performance or takes too long.
I keep losing time and causing performance jitters on the cluster whenever I need to extract large sets of _ids or specific fields from big indices for audit or reporting. It feels like I'm always brute-forcing it. What habit should I change to perform these large data extractions more efficiently and with minimal impact on live services?
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.