◆ Elasticsearch

Combine MUST and OR in Elasticsearch bool 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 taskCombine the existing 'MUST' clause for 'status:active' with an 'OR' clause for 'priority:high'…+
Combine the existing 'MUST' clause for 'status:active' with an 'OR' clause for 'priority:high' or 'priority:urgent' in the 'support_tickets' index. Return the top 10 results.
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 combine the 'MUST' and 'OR' clauses for the support tickets, show me the current query…+
Before I combine the 'MUST' and 'OR' clauses for the support tickets, show me the current query and then propose a new query that ensures tickets are active AND either high or urgent. Also, tell me if there are any performance implications of adding this 'OR' clause to such a large index.
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 build a query for the support team that finds active tickets 'MUST' have an…+
I'm trying to combine complex clauses, and the results are not what the support team expects.
I'm trying to build a query for the support team that finds active tickets 'MUST' have an assignee AND 'OR' be overdue or unassigned. The current query I've written is returning too many irrelevant results, and the team is frustrated they can't find what they need. I'm afraid of misinterpreting their logic and delivering incorrect data. What's the best way to structure this complex boolean query to precisely match their requirements?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI'm constantly struggling to translate complex business logic from stakeholders into precise…+
I consistently struggle to translate complex business logic into precise Elasticsearch boolean queries.
I'm constantly struggling to translate complex business logic from stakeholders into precise boolean queries, especially when 'MUST', 'SHOULD', and 'MUST_NOT' are involved. It feels like I'm always iterating and getting it wrong the first few times. What habit should I change to more effectively design and test these complex queries from the outset, ensuring they accurately reflect user intent?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

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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.