Most job descriptions are archaeology — layers of requirements copied from the last posting, which copied the one before. This playbook rewrites a stale JD in three passes: plain-language duties, a hard cut of requirements down to what the job actually needs, and a sweep for language that quietly tells good candidates not to apply.
Workflow area: HR & Recruiting · Tier: Core · Hands-on time: ~30 minutes
A bloated JD costs you twice: qualified people self-select out (research consistently shows many candidates — women especially — skip roles unless they meet nearly every listed requirement), and unqualified people apply anyway because the wall of bullets says nothing concrete. AI is a strong first-pass editor here because the three fixes — clarity, requirement discipline, bias language — are pattern problems, and patterns are what models see.
Collect the inputs. The current JD, plus two answers from the hiring manager: "What will this person actually do in a normal week?" and "Think of your best performer in this role — what did they have on day one?" Those two answers outrank everything in the old JD.
Run the diagnosis pass first — no rewriting yet.
Here is a job description. Do not rewrite it yet. Diagnose it:
1. JARGON AND VAGUENESS: list every phrase a candidate outside our
company could not act on (e.g. "drive synergies", "wear many
hats") — with a plain-language alternative for each.
2. REQUIREMENT AUDIT: for every requirement, classify it as
ESSENTIAL (job fails without it on day one), TRAINABLE (could
learn in 3 months), or DECORATIVE (habit, not need). Explain
each classification in one line.
3. BIAS SWEEP: flag gender-coded language (e.g. "rockstar",
"dominant", "nurturing"), age signals ("digital native",
"recent graduate"), unnecessary physical/education/pedigree
requirements, and culture-fit phrases that read as "people
like us".
4. MISSING BASICS: is salary range, location/remote policy,
reporting line, or a concrete description of success absent?
Job description:
[PASTE JD]
Context from hiring manager — a normal week: [ANSWER 1]
What the best performer had on day one: [ANSWER 2]
Review the diagnosis with the hiring manager. This is the human checkpoint. Typical output worth acting on:
REQUIREMENT AUDIT:
- "5+ years in a similar role" — DECORATIVE: the weekly duties
described are achievable at 2-3 years; the number filters
career-switchers with equivalent skills.
- "Advanced Excel including pivot tables" — ESSENTIAL: three of
five weekly duties involve reporting from spreadsheets.
- "MBA preferred" — DECORATIVE: no listed duty requires it.
BIAS SWEEP:
- "aggressive self-starter" — masculine-coded; try "proactive
and independent".
Accept, reject, or override each finding — the model proposes, the manager decides.
Rewrite with the agreed decisions locked in.
Now rewrite the JD applying only the changes we agreed:
[PASTE YOUR ACCEPTED/REJECTED DECISIONS]
Structure:
- Title: plain and searchable (what candidates type, not internal
grade names)
- Hook: 2-3 sentences on what this role owns and why it matters
- "What you'll do": 5-7 bullets, each starting with a verb,
describing a normal week
- "What you'll need": ESSENTIAL requirements only, max 5
- "Nice to have": TRAINABLE items, max 3, explicitly optional
- "What success looks like": 3 outcomes at 6 months
- Salary range, location, reporting line
Tone: direct, warm, no superlatives. Reading level: accessible to
a candidate skimming on a phone. Keep our legal/EEO block verbatim:
[PASTE BLOCK]
Run the candidate-eye test. Ask in the same chat: "Read this as a strong candidate with 80% of the requirements. What would make them hesitate to apply?" Fix anything real it surfaces, then have one actual human outside HR read it in under a minute and tell you what the job is. If they can't, the hook failed.
Do not paste internal compensation bands, performance data about the incumbent, or reasons the last person left into a consumer AI tool. The JD itself is public-bound content; the context around it often isn't. Genericize or use your enterprise tenant — pb-12 covers the full pre-flight.
The model finds the decorative requirements and coded language faster than any human reviewer — but the cut list is a hiring decision, and hiring decisions stay human. Diagnose with AI, decide with the manager, then rewrite.