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Apply Elasticsearch index best practices

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 taskSet the number of primary shards for the new 'sensor_readings_daily' index to 5 and replicas to…+
Set the number of primary shards for the new 'sensor_readings_daily' index to 5 and replicas to 1.
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 we go live with the new 'iot_devices' data stream, analyze the expected daily ingest…+
Before we go live with the new 'iot_devices' data stream, analyze the expected daily ingest rate of 1TB and the query patterns for the last 3 months. Recommend an optimal shard count and lifecycle policy to ensure efficient storage and search performance for data retention of 90 days.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentOur 'metrics_archive' index is growing much faster than expected, and we're almost out of disk…+
Our 'metrics_archive' index is growing much faster than expected, and we're running out of disk space.
Our 'metrics_archive' index is growing much faster than expected, and we're almost out of disk space on the data nodes. I'm not sure if it's too many shards, too few, or if the retention policy isn't working. I'm afraid to just add more nodes without understanding the root cause. What's the most likely reason for this rapid growth, and what's the safest, most cost-effective next step to manage this before we hit a hard limit?
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 re-sharding or re-indexing old data because of poor initial planning, which…+
I'm constantly re-sharding or re-indexing old data because of poor initial planning.
I'm constantly re-sharding or re-indexing old data because of poor initial planning, which wastes precious time and compute. It's hard to get it right the first time, especially with unpredictable data growth. What habit should I change in my index design process to avoid these costly rework cycles and build more scalable indices 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.