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AI for HR and Hiring

Job ads, candidate correspondence, interviews and onboarding with a language model — and the decisions it must never be handed

AI for HR and Hiring

About this course

A course for recruiters, HR generalists and hiring managers — people whose working day is made of job ads, inboxes full of CVs, interview notes and onboarding plans. It covers the part of that day where a language model genuinely helps: turning a wish list of requirements into a description of the actual job, drafting an invitation and a rejection that does not humiliate anyone, preparing questions from one specific CV instead of a generic list, turning interview scribbles into a comparison you can defend, building a first-weeks plan, and reading three hundred free-text survey answers without flattening them into nothing.

The other half of the course is the line the tool must not cross. Hiring is a set of decisions about people, and a model does not make them: it prepares text and organises information, while the screening, the rejection and the assessment stay with a human being who answers for them. One full lesson is spent on bias — not as a warning but as a mechanism, because a model reproduces the patterns in the data it learned from, and an automated CV score can quietly push out people for their gender, their age, a gap in their record or the name of their university. Another is spent on candidate personal data, which a person entrusted to you and not to a chat service.

No programming required, and nothing is tied to a particular model version or applicant tracking system. Most lessons carry a prompt you can copy, often as a pair — a weak request and a stronger one for the same job. The course makes no promises about your time-to-hire; it shows what the tool does well, what it does badly, and where using it at all is a risk you are taking on knowingly.

Course curriculum

  1. Decisions about people: the boundary of the tool

    What a language model does in hiring and what it never decides, how bias gets into a CV score, and what a candidate's personal data means in practice.

    • 1.What the model does, and what stays yours12 min
    • 2.Bias: how a CV score learns to reject people18 min
    • 3.A CV is someone else's personal data14 min

  2. The job ad, the role and the funnel

    Writing a job ad from the real tasks, describing a role with grades and competencies, and corresponding with candidates from invitation to rejection.

    • 1.A job ad that describes the job15 min
    • 2.Role description, grades and a competency matrix16 min
    • 3.Writing to candidates: invitation, salary, rejection15 min

  3. Interviews: preparing and making sense of them afterwards

    Questions built from one specific CV, checking claimed experience, a structure that holds, and turning scribbled notes into a comparison you can defend.

    • 1.Preparing for an interview with this CV in front of you16 min
    • 2.After the interview: scribbles into a comparison14 min

  4. After the offer: onboarding, communication and analytics

    A first-weeks plan and internal documents, reading free-text survey answers without flattening them, building training, and where people analytics stops being informative.

    • 1.Onboarding, checklists and internal documents16 min
    • 2.Surveys, training and the limits of people analytics18 min