An HR prompt for interview-question drafts bans demographic assumptions and focuses on job competencies. What effective inclusive practice is shown?

An HR prompt for interview-question drafts that bans demographic assumptions and focuses on job competencies shows inclusive prompting practice for municipal hiring. City HR must center lawful, job-related criteria tied to the posted classification. Explicitly forbidding demographic stereotypes and requiring competency focus reduces biased question generation, supports equal-employment norms, and keeps drafts usable for structured panel interviews. Algorithmic debiasing that masks demographic attributes and prioritizes job competencies in municipal models is a model-adjustment practice, not a prompting practice; it fails as the inclusive practice for drafting interview questions. Adversarial red-teaming that tests demographic assumptions and evaluates job competencies for HR algorithms is an adversarial testing practice for HR algorithms, not a prompt design for drafting interview questions; it fails as the inclusive practice for drafting interview questions. Data sanitization pipelines that strip demographic labels and preserve job competencies for municipal datasets are a dataset-preparation practice, not a prompting practice; they fail as the inclusive practice for drafting interview questions. Exam caveat: inclusive prompting helps draft quality but does not replace job analyses, structured interview protocols, scoring rubrics, or human HR and equity review before questions are used. Operational check: include standing bans on protected-class inferences in the HR prompt kit; require each question mapped to a posted competency; have HR and equity officers approve banks before use; and document that generative drafts are starting points, not automatic interview scripts.