◆ Elasticsearch

Perform a partial match 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 taskPerform a partial match query for all customer support tickets containing 'login issue' in the…+
Perform a partial match query for all customer support tickets containing 'login issue' in the description field for the last 7 days.
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 partial match query for 'login issue' in customer support tickets, ensure…+
Before I run this partial match query for 'login issue' in customer support tickets, ensure it's fuzzy enough to catch common misspellings like 'loggin' or 'log-in' but not so broad it returns irrelevant results. The support manager needs accurate data for Friday's meeting.
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 partial match query I ran for 'login issue' in customer support tickets is returning too…+
The partial match query for 'login issue' is returning too many irrelevant tickets.
The partial match query I ran for 'login issue' in customer support tickets is returning too many irrelevant results, making it useless for the support manager's report. I'm afraid of missing critical tickets if I narrow it too much, but the current results are noise. I can't tell if the problem is the query's fuzziness or the data itself. What's the best next move to refine this query to get accurate results for the support manager by end of day?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI frequently lose credibility with the business teams because my partial match queries either…+
My partial match queries often return too much noise or miss relevant data, making reports unreliable.
I frequently lose credibility with the business teams because my partial match queries either return too much noise or miss crucial data, making their reports unreliable. It feels like a constant struggle to find the right balance. What habit should I change to consistently build more precise and effective partial match queries 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.