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  2. 7 Ways AI Can Create or Tweak Your Resume Before Your Next ...

    www.aol.com/7-ways-ai-create-tweak-135700156.html

    2. Align the resume with job market trends. A good resume should align with changing market trends. ChatGPT can help you find information about the latest trends for your industry or role.

  3. 20 Productivity Tech & AI Tools Perfect for Freelancers

    www.aol.com/20-productivity-tech-ai-tools...

    The AI tool can also generate a resume summary to guide you when writing a cover letter. An example of a ChatGPT prompt to use in this situation is, “Create a cover letter that highlights my ...

  4. I’m a Career Expert: Here’s Why You Should Put AI on Your Resume

    www.aol.com/m-career-expert-why-put-160017502.html

    “While having AI skills on your resume shows your ability to add to a company’s AI initiatives and is currently seen as an advantage, as things evolve, it will soon become an expectation ...

  5. Applicant tracking system - Wikipedia

    en.wikipedia.org/wiki/Applicant_tracking_system

    An Applicant tracking system has 8 main use cases: Source qualified candidates, Job posting, Parse resumes, Filter and search candidate database, Rank and rate candidates, Manage and track applicants, Schedule applicant interviews, Communication tools like automated emails and reminders to candidates and hiring manager, Reporting and analytics ...

  6. ZipRecruiter - Wikipedia

    en.wikipedia.org/wiki/ZipRecruiter

    ZipRecruiter was founded in 2010 by Ian Siegel, [15] Joe Edmonds, Ward Poulos and Will Redd. [16] [17]In June 2015, as the company began growing, they opened an R&D center in Israel and in 2018, claimed to have developed an artificial intelligence algorithm to increase the accuracy of job seeker/employer matches.

  7. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.

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