How to Write Better Job Descriptions With AI

TL;DR: Job descriptions are where the AI for HR hub tells teams to start, and for good reason: they’re pure language work, nobody is affected until a human approves the text, and the win is visible in the first week. But the value isn’t just speed. Used as an editor rather than only a drafter, AI audits the requirements list that quietly shrinks your pipeline, flags exclusionary phrasing, and keeps fifty postings consistent with your leveling framework. This guide covers the drafting workflow, the inclusive-language pass, the accuracy checks, and the two failure modes, invention and blandness, that separate useful postings from sludge.

Why this is the right first workflow

A job description sits upstream of everything: it shapes who applies, it’s the criteria source for resume screening, and it feeds the interview kit. It’s also a legal artifact, the documented, job-related requirements you’d point to if a hiring decision were ever challenged, and in ADA terms the record of a role’s essential functions. Getting JDs right is therefore both a recruiting improvement and groundwork for every higher-risk workflow that follows.

The risk profile is low but not zero. Two things can go wrong:

• Fabrication. A large language model asked to “write a job description for a Senior Data Engineer” will confidently produce duties, tool lists, benefits, and requirements it invented, a classic hallucination problem. If those ship, you’re screening candidates against fiction and promising benefits you don’t offer.
• Exclusion by template. Models trained on the internet’s job postings reproduce the internet’s habits: degree requirements nobody needs, “rockstar/ninja” phrasing, years-of-experience thresholds that function as age proxies, and physical requirements (“must lift 50 lbs”) pasted into desk jobs. AI can amplify these defaults or audit them, depending entirely on how you prompt it.

The workflow

Step 1: Collect real inputs before prompting

Ten minutes with the hiring manager beats any amount of prompt engineering on an empty prompt. Capture:

• What this person will actually do in the first 90 days and first year, outcomes, not activities
• The 3-5 capabilities that genuinely predict success, and how each would be demonstrated
• Team context: who they work with, report to, what’s hard about the problem
• The approved salary range, actual benefits, and location/remote policy (from HR systems, never from the model)
• Your leveling framework entry for this role, if you have one

Step 2: Draft from your inputs, not the model’s memory

Example prompt (drafting): “Draft a job description using ONLY the information below, do not add duties, requirements, benefits, or company claims that aren’t listed. Structure: one-paragraph role summary focused on what the person will accomplish; ‘What you’ll do’ (5-7 outcome-oriented bullets); ‘What you’ll need’ (only the must-haves I listed); ‘Nice to have’ (kept short); compensation and benefits exactly as provided. Plain language, no clichés (‘fast-paced’, ‘wear many hats’, ‘rockstar’), 8th, 10th grade reading level. Inputs: [paste everything from step 1].”

The “ONLY the information below” constraint is the whole game. It converts the model from an inventor into a writer.

Step 3: Run the requirement audit

This is the pass most teams skip and the one with the most pipeline impact. Inflated requirements don’t just narrow the funnel, they narrow it unevenly. Degree requirements screen out capable candidates who took non-traditional paths; long years-of-experience minimums correlate with age; and there’s a well-known pattern that many candidates, disproportionately women, don’t apply unless they meet essentially every listed requirement. Every unnecessary line costs you people who could do the job.

Example prompt (requirement audit): “Review the requirements in this draft. For each one: (1) Is it a genuine must-have for day-one success, or something learnable in the first three months? (2) Is it the capability itself, or a proxy, e.g., a degree standing in for analytical skill, years-of-experience standing in for a competency, specific-company experience standing in for domain knowledge? (3) Could it disproportionately exclude candidates by age, disability, caregiving history, or educational background? Recommend: keep as must-have, move to nice-to-have, rewrite as a direct capability, or cut.”

Expect the audit to move a third of a typical requirements list. The hiring manager arbitrates, some proxies survive scrutiny, but now it’s a decision, not a default.

Step 4: Run the inclusive-language pass

Example prompt (inclusion pass): “Review this job description for language that could discourage qualified candidates: gender-coded words, age proxies, unnecessary physical-ability language, idioms that assume cultural context, and jargon. For each flag: quote it, explain who it may exclude and why, and propose a replacement that preserves the actual requirement.”

Treat the output as flags for human judgment, not automatic rewrites. AI catches phrasing; it can’t know that a physical requirement is genuinely essential to this role, that’s an accuracy and ADA question the hiring manager and HR answer.

Step 5: Verify, then ship

The named reviewer (normally the hiring manager) confirms before posting:

1. Every duty and requirement is true for this role as it exists today, not the role three years ago, not the template.
2. Compensation and benefits match the approved figures. Where pay-transparency laws apply, the range is a compliance item; get it from HR, never from the model.
3. Essential functions are stated accurately, this matters for disability accommodation down the line.
4. It sounds like your company, not like every posting on the internet. If it’s generic, the fix is more real input, not more adjectives.

Scaling it: consistency as the second win

Once the single-JD workflow runs, the compounding value is portfolio-level:

• Leveling consistency. Paste your leveling framework plus a batch of postings and ask AI to flag mismatches, a “senior” posting demanding staff-level scope, two same-level roles with wildly different requirement bars. Inconsistent postings create inconsistent hiring and, later, pay-equity questions.
• A living template library. Keep approved postings as the input for future ones, “start from our approved Backend Engineer II posting; here’s what’s different about this role”, so quality survives recruiter turnover.
• Refresh cadence. Postings rot. A quarterly AI-assisted pass comparing live postings against current team reality catches drift cheaply.

Set the ground rules, which tool, which data may be pasted, who reviews, in your AI acceptable use policy, and slot this workflow into month one of the AI adoption roadmap. It’s the confidence-builder the rest of the HR cluster stands on.

FAQ

Is it OK for a job posting to be AI-written? Yes, provided a human verifies every duty, requirement, and benefit is true for the specific role. The posting is the reference document for the whole hiring process and can carry legal weight (essential functions, pay-transparency ranges), so accuracy is the bar, not authorship.

Can AI actually make job descriptions more inclusive? It’s a strong flagging layer for gender-coded language, age proxies, unnecessary degree and physical requirements, and jargon. A human makes the final call, and wording passes can’t fix a requirements list that is itself exclusionary, run the requirement audit too.

Should we disclose salary ranges, and can AI decide them? Where pay-transparency laws apply you must post ranges; that number comes from HR and counsel. Never let a model generate or estimate compensation, it will hallucinate something plausible. Provide the approved range as input.

How do we keep AI job descriptions from all sounding the same? Feed it what only your team knows: first-quarter outcomes, team context, what makes the problem hard. Generic input yields generic output; the fix for blandness is better input, not a fancier prompt.

Want to know if your team is ready to move from job descriptions to the higher-stakes HR workflows? The free AI readiness assessment gives you a prioritized answer in about ten minutes.