| ▲ | drillsteps5 3 hours ago | |
Several months ago, having exhausted all other options in fruitless job search, I asked for assistance of a recruiter who I used to work with some time ago. Just to get some pointers on how I could improve my search, with his experience working on the other side. His advice on "tailoring" was very simple. Go to the most popular LLM (ie Claude). Give it the job description. Give it your resume. Ask it for percentage match, it will give you a number. Then ask for advice on improving your resume, you can either ask to rework the resume by itself (if you're in a hurry) or you can work with the thing to modify the sections/entries yourself, one by one, if you have time. With each modification you will get better and better percentage match. Ideally you need to get to high 80% or maybe even 90%. The idea is that automated ranking tools used by recruiters/HR are ultimately use the very same LLM, so you are improving your ranking assigned to your resume by those ranking systems. I have to add that now this advice is mostly useless as everybody's using the same technique so you won't be standing up against others but merely be on the same level. Add to it the fact that many job descriptions are inaccurate and sometimes downright misleading, and hiring managers use their own criteria, and you will understand why connections/referrals is probably the only way to get hired now. | ||
| ▲ | Terr_ 2 hours ago | parent [-] | |
> The idea is that automated ranking tools used by recruiters/HR are ultimately use the very same LLM, so you are improving your ranking assigned to your resume by those ranking systems. Working in HR-tech, that is definitely happening, and "self-bias" is a thing with LLMs so if you can guess what the employer/screening-process is using then that's a bonus. That said, we should distinguish between "Hey robot tell me if X matches Y" prompts, versus model-weights comparison. In the second case, the system feeds your resume into a model with no prompt, grabs all the magic math numbers, and statistically compares them to the magic-numbers if the model had been given some "ideal" resumes. | ||