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Screen candidates and summarize resumes with AI

Commenters describe AI used to summarize resumes, mass-screen candidates, skip boolean queries, and an agent that screens candidates anytime.

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Use case overview

Commenters describe AI used to summarize resumes, mass-screen candidates, skip boolean queries, and an agent that screens candidates anytime.

Reported experience

  1. What they did

    Commenters said the common live use is lighter-weight than the hype: AI summarizes resumes, helps skip or build Boolean sourcing queries, mass-screens obvious low-fit applicants, and in one case ranks CV batches against job descriptions with match analysis. One recruiter described a homegrown setup that grabs screening info from the page, sends it to a local Ollama qwen3:8b model, auto-navigates past poor matches, and leaves the filtered shortlist for manual review.

  2. How it went

    The thread’s overall verdict is mixed: several recruiters said AI still screens resumes poorly, and one company’s AI phone-screen pilot bombed. The clearest positive result reported was the local-screening workflow, where about 90% of the candidates it separated were said to be good fits.

  3. Worth knowing

    The main catch is that recruiters in the thread still kept humans in the loop for final review; even the strongest example used AI mostly to clear shortlist noise, not make hiring decisions.

Example prompts

Try with Claude Cowork
I have resumes in [folder path] for the role of [job title]. Read each one and make a table with candidate name, years of relevant experience, key skills, and a match rating against these requirements: [must-haves and nice-to-haves]. Sort by fit, flag any you are unsure about with a reason, and stop once the table is saved as [spreadsheet name]. Do not email any candidates.