◆ Elasticsearch

Avoid common configuration mistakes

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'm setting up a new 'user_profiles' index. Apply the standard template for user data, ensuring…+
I'm setting up a new 'user_profiles' index. Apply the standard template for user data, ensuring it has dynamic mapping disabled for new fields and a default shard count of 3, then confirm the settings.
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 deploy the new 'customer_interactions' index, check its proposed configuration against…+
Before I deploy the new 'customer_interactions' index, check its proposed configuration against our best practices. Flag any settings that could lead to high memory usage, excessive shard overhead, or slow indexing rates given our typical data volume.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentAfter deploying the new 'transaction_logs' index, I'm seeing unexpectedly high CPU usage, even…+
I'm seeing high CPU usage after deploying a new index, but the settings look standard.
After deploying the new 'transaction_logs' index, I'm seeing unexpectedly high CPU usage, even though the configuration looks standard and data volume isn't extreme yet. My manager is asking why we're already scaling up. I'm afraid I've missed a subtle interaction. What's the most common configuration mistake that leads to high CPU on a new index, and what specific setting should I scrutinize first?
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 encounter performance issues from subtle configuration oversights on new indices,…+
I frequently encounter performance issues from subtle configuration oversights on new indices.
I frequently encounter performance issues from subtle configuration oversights on new indices, which then requires emergency fixes and explanations to the team. This erodes my credibility. What habit should I change in my index setup process to catch these non-obvious configuration problems before they impact production, especially regarding mapping or shard allocation?
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.