BXUP Track progress & test yourself — free
AI at Office · Playbook · Core · ~3 min read
AI AT OFFICE · PLAYBOOK · CORE

Rewrite Job Descriptions for Clarity and Inclusion

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.

When to use — and when not to

  • Use it before every posting goes liveeven a "fine" JD usually loses a third of its bullets and gains a real first-line hook.
  • Use it on your whole JD librarybatch-rewriting ten stale descriptions in an afternoon standardizes voice and structure across the company.
  • Use it when a role attracts the wrong applicantsmismatched pipelines usually trace back to vague duties and inflated requirements.
  • Skip the final call on requirementsAI can flag that "MBA required" looks unnecessary for a coordinator role; only the hiring manager can confirm it. Every cut is a human decision.
  • Skip legal boilerplate rewritesEEO statements, notice-period clauses, and compensation-disclosure language follow your jurisdiction and your legal team, not a model's preference.

The recipe

  1. 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.

  2. 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]
    
  3. 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.

  4. 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]
    
  5. 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.

Make it yours

  • Batch mode for the JD library: paste your JD template decisions once, then feed ten old JDs in sequence in one chat — "same structure, same tone rules" — and you get a consistent library instead of ten voices.
  • Google Docs workflow: keep the diagnosis table in a comment-enabled doc so hiring managers resolve each finding asynchronously — the accepted decisions become your step-4 paste.
  • Internal mobility variant: rewrite the same role twice — external posting and internal-transfer version — from one diagnosis; the internal one swaps the company pitch for team context and keeps requirements identical, which keeps internal candidates on equal footing.

Quality check

  • Every remaining requirement is defensibly essentialthe hiring manager, not the model, confirmed each one.
  • A candidate can picture a Tuesdayduties describe actual work, not aspirations.
  • The bias sweep was reviewed, not just acceptedmodels over-flag and under-flag; a human judged each item.
  • Salary and location are statedomitting them costs more applicants than any wording fix recovers.
  • Legal blocks are untouchedverbatim from your approved language.
Warning

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.

Key Takeaway:

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.

This is the free reading view. Open BXUP to track your progress, run the hands-on labs, and prove it with original, exam-pattern assessments.
© Anvi Innovate · BXUP — Be Exponentially Upskilled · BXUP is an independent learning platform, not affiliated with or endorsed by AWS, Microsoft, Google, NVIDIA, IBM, Salesforce, Databricks, Oracle or Anthropic. All trademarks belong to their respective owners. · bxup.in