AI in Recruiting

Glossary

Terms TA leaders actually need when evaluating AI workflows

Copilot pattern

The recruiter approves the decision; the model drafts or explores. In the session we mark which steps are draft-only and which need a named reviewer before anything goes external.

Structured output

Asking the model to return machine-parseable fields (JSON tables, scorecard sections) so recruiters compare candidates consistently instead of chasing paragraphs.

Grounding

The model works from pasted notes, JD text, or rubrics you provide instead of guessing from the open web. That is what makes role briefs and HM alignment memos usable.

Retrieval-augmented workflow

Pull snippets from your own docs or templates before generation. Often: intake notes, brand lines, past scorecard examples. Candidate PII only where policy and systems allow.

PII minimization

Share only the candidate fields needed for the task. Redact, pseudonymize, and keep bank-grade or health data in systems built for them.

Human-in-the-loop

Checklist steps before send: second read on outreach, HM memo, or rejection language. We write these as boxes to tick, not slogans on a slide.

Evaluation rubric

A short list for “good enough” output: factual fit to notes, tone, completeness, bias red flags. Recruiters run the list in a few minutes per draft.

Prompt library

Versioned, team-owned prompts with examples and guardrails. This is what survives after the session, not a PDF nobody opens.

Model drift

Behavior changes when providers ship new models. Mitigation: pinned templates, monthly spot checks, and a dated refresh on the prompt library.

Automation boundary

Written list of steps that never run on autopilot (final reject wording, compliance-sensitive calls) versus steps that can speed up with a human sign-off.

Work sample / scenario task

A hiring signal based on doing real work. The session covers how to write tasks so you measure the candidate, not the hidden model draft they might have used.

Hiring manager alignment packet

One-pager: outcomes, constraints, must-haves, interview plan. AI can draft bullets; the HM still has to answer the hard trade-off questions.

Sourcing coverage

How many channels and angles you actually work. AI can suggest more variants; a human still picks channels and approves messaging.

Audit trail

Who read which draft, what changed, and what went to the candidate or HM. Matters when AI helped on screening notes or pipeline summaries.

Vendor vs workflow

New software rarely fixes adoption on its own. The workshop sequences handoffs, artifacts, and SLAs so the team can swap tools later without starting from zero.