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The main benefit of using an LLM with job applications is that the LLM can optimize the text of a resume to match the job description and improve ATS scores. Many companies reject applications with less than 90% of the job description key phrases also showing up in the CV.
As currently coded, as far as I can tell, the LLM isn't passed the job description or recruiter information when it writes the resume. The only thing I see getting passed is the resume format on the first line of strings_feeder-cr.py.
Second, rather than three rigid points from the CV, the LLM should dynamically decide how many points (probably no less than 3 no more than X) should be added to each job in the job history so that it can achieve the 90% mark.
I'd be happy to rewrite the prompt to address this but I'm not clever enough with python to figure out how to pass the job description stuff that the bot pre-processes in the other project into the strings_feeder-cr file so that the LLM has enough information to work from.
The text was updated successfully, but these errors were encountered:
The main benefit of using an LLM with job applications is that the LLM can optimize the text of a resume to match the job description and improve ATS scores. Many companies reject applications with less than 90% of the job description key phrases also showing up in the CV.
As currently coded, as far as I can tell, the LLM isn't passed the job description or recruiter information when it writes the resume. The only thing I see getting passed is the resume format on the first line of strings_feeder-cr.py.
Second, rather than three rigid points from the CV, the LLM should dynamically decide how many points (probably no less than 3 no more than X) should be added to each job in the job history so that it can achieve the 90% mark.
I'd be happy to rewrite the prompt to address this but I'm not clever enough with python to figure out how to pass the job description stuff that the bot pre-processes in the other project into the strings_feeder-cr file so that the LLM has enough information to work from.
The text was updated successfully, but these errors were encountered: