◆ Elasticsearch

Filter by array field size

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 find all the documents where the 'tags' field has exactly 3 entries. This is for the…+
I need to find all the documents where the 'tags' field has exactly 3 entries. This is for the 'products' index, so I can review the data quality before the Friday demo.
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 hand this over to the analytics team, help me refine the query for the 'products'…+
Before I hand this over to the analytics team, help me refine the query for the 'products' index. I need to filter by the size of the 'tags' array field, but also make sure it's performant enough for large datasets. Flag any potential performance bottlenecks or alternative approaches.
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 new product manager just asked for a report on products with more than 5 tags, but the…+
The new product manager just asked for a report on products with more than 5 tags, but the existing query is timing out.
The new product manager just asked for a report on products with more than 5 tags, but the existing query on the 'products' index is timing out. I'm afraid to tell her it'll take days to run, and I don't know the best way to optimize this for such a large array size without breaking other queries. What's the most efficient way to filter by array size here, and what's the immediate next step to get her something quickly?
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 credibility when clients ask for reports based on the size of array…+
I keep struggling with efficient queries on array field sizes.
I keep losing time and credibility when clients ask for reports based on the size of array fields, especially on our largest indexes. My current approach often leads to slow queries or timeouts. What habit should I change to consistently build more performant queries for array field size filtering from the start, so I'm not scrambling every time?
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.