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Assess AI-driven recruiting power user

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 taskFind me five senior software engineers with expertise in machine learning and Python, based in…+
Find me five senior software engineers with expertise in machine learning and Python, based in San Francisco, who are open to new opportunities and have at least 8 years of experience.
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 reach out to these candidates, identify what makes them strong fits for our Senior ML…+
Before I reach out to these candidates, identify what makes them strong fits for our Senior ML Engineer role. Look for specific projects or contributions that show leadership potential and a track record of shipping production-ready models.
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 just sent a personalized message to a highly sought-after candidate, highlighting our unique…+
I just got a 'not interested' reply from a top candidate after what I thought was a compelling initial message.
I just sent a personalized message to a highly sought-after candidate, highlighting our unique culture and impact, but got a polite 'not interested' back. I'm afraid my outreach isn't cutting through the noise, or I'm missing something critical in their profile. What's the likely diagnosis, and what's my best next move to re-engage or find similar profiles?
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 manually reviewing profiles, trying to identify niche skills and relevant…+
I'm spending too much time manually sifting through profiles to find relevant experience and skills, often missing key details.
I keep losing time manually reviewing profiles, trying to identify niche skills and relevant project experience. I'm worried I'm overlooking strong candidates because I'm not seeing the full picture quickly enough. What habit should I change to improve my efficiency and accuracy in candidate assessment?
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 LinkedIn'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.