Job description readability
How clearly and quickly a job posting communicates the role, requirements, and benefits to a qualified candidate. Plain language, short paragraphs, and bias-free wording increase apply rates and reduce mismatched applications.
Michal Juhas · Last reviewed June 14, 2026
What is job description readability?
Job description readability measures how fast and clearly a posting communicates what a role is, who it is for, and why someone should apply. A recruiter can have the right keyword set and still lose qualified applicants because the JD reads like an internal requirements document rather than a compelling invitation. Readability tools and AI prompts help identify passive voice, excessive length, credential inflation, and language that skews away from certain groups.
In practice
- A sourcer copies a JD into a ChatGPT prompt asking for passive voice flags and a Flesch-Kincaid estimate. The output highlights 11 bullet points that start with "responsible for" and suggests active verb replacements.
- A TA ops lead runs every new JD through a shared Notion template checklist: job title, day-to-day tasks, required versus preferred criteria, and one compelling reason to apply. If a section is missing, it goes back to the hiring manager before posting.
- On a hiring manager call, a recruiter hears "I need someone proactive and driven." The recruiter translates that into specific, testable criteria in the JD rather than the buzzwords, which would have filtered out good candidates who don't self-identify with those adjectives.
Quick read, then how hiring teams use it
This is for recruiters, TA leads, and HR partners who review or approve JDs before they go live. Skim the first section when you need a shared vocabulary for a review meeting. Use the second when you are setting standards or auditing a JD batch.
Plain-language summary
- What it means for you: A readable JD converts more qualified visitors to applicants and fewer unqualified ones, reducing inbound noise and improving your pipeline quality from day one.
- How you would use it: Run every new JD through a plain-language checklist or AI prompt before posting. Flag credential inflation, passive voice, and anything that reads like a legal waiver.
- How to get started: Pick one JD posted in the last 90 days that produced a weak applicant pool. Run it through the two-minute scan test. List what information a qualified candidate could not find. Rewrite those sections and compare the next 30 days of applicant quality.
- When it is a good time: Before a JD goes live, not after apply rates disappoint. Set a 15-minute readability review as a standard gate in your intake process.
When you are running live reqs and tools
- What it means for you: Readability is a conversion rate lever at the top of the funnel. Improving one JD is an experiment; building a template governance system is a durable efficiency gain for every recruiter on the team.
- When it is a good time: When you are building or auditing JD templates, after a new req opens and before it hits the ATS, and when a role repeatedly attracts the wrong applicant profiles.
- How to use it: Connect JD drafting to your intake-to-JD AI workflow. Use a prompt that asks the model to flag gendered language, passive constructions, and lines a candidate already assumes. Store approved templates in your ATS or a shared repo so the governance is not manual.
- How to get started: Draft a blocked-phrases list with the hiring manager you work with most. Add five phrases that historically correlate with weak inbound, and five active-verb alternatives. Make that the starting point for your JD template library.
- What to watch for: Over-editing until the JD reads like no one wrote it. The goal is clarity, not corporate blandness. If every JD at your company sounds identical, candidates notice. Maintain role-specific voice within a readable structure.
Where we talk about this
On AI with Michal sessions, JD readability comes up in the intake-to-brief workflow in the AI in recruiting track. We work through live JDs from attendees, run them through readability prompts, and produce a cleaner version in the same session. If you want the full practical workflow, start at the workshops page and bring a JD you actually need to post.
Around the web (opinions and rabbit holes)
Third-party creators move fast. Treat these as starting points, not endorsements, and double-check anything before wiring candidate data.
YouTube
- How to Write a Job Description That Attracts the Best Candidates (SHRM) covers the fundamentals of job description structure for practitioners.
- Writing Inclusive Job Descriptions (LinkedIn Talent Solutions) walks through common language pitfalls with before-and-after examples.
- Why do job descriptions suck so much? in r/recruiting is a frank thread from both sides: recruiters who inherit bad templates and candidates who cannot decode them.
- Tips for writing better JDs? in r/humanresources collects practitioner tactics on credential inflation and plain-language rewrites.
Quora
- What makes a good job description? surfaces recruiter and candidate perspectives that rarely appear in vendor whitepapers.
JD red flags versus good signals
| Signal | Red flag | Good signal |
|---|---|---|
| Length | Over 800 words | Under 500 words |
| Opening section | Company history | Day-to-day tasks |
| Requirements | "Degree required" for non-specialist roles | "Experience with X" |
| Language | Passive voice, adjective stacks | Active verbs, specific criteria |
| Apply rate | Under 5% of visitors | Over 10% of visitors |
Related on this site
- Glossary: Intake to JD AI, AI bias audit, Job description bias detection, Scorecard, Human-in-the-loop
- Blog: Boolean search vs AI sourcing
- Guides: Sourcers
- Live cohort: AI in recruiting workshop
- Course: Starting with AI: the foundations in recruiting
- Membership: Become a member
Frequently asked questions
Why does readability matter more than keyword density?
Keywords help a JD appear in search, but readability decides whether a qualified person applies. A wall of bullets, generic phrases, and passive voice signal corporate speak to candidates who have options. Sourcers find that high-volume roles attract more relevant applications when the JD is under 500 words, uses active verbs, and puts the most interesting parts in the first screen. AI tools can flag complexity scores, passive constructions, and length before you publish. A/B tests from job board providers consistently show shorter, clearer postings outperform keyword-stuffed versions on apply rate, even holding job title constant. Fix the read; the ranking follows.
What does bias in a JD actually look like?
Bias shows up in three ways: gendered language (words like "aggressive" or "nurturing" that skew male or female perception of fit), credential inflation (requiring a degree for a role that does not need one), and implicit culture signals such as "rockstar" that correlate with age or personality. AI tools trained on demographic response data can flag high-risk phrases before you post. Audit every JD against your own hire quality data: if qualified candidates from underrepresented groups apply at lower rates despite matching criteria, check language first. Your AI bias audit process should include JD text as a review input.
How do AI tools help with JD readability without introducing new bias?
Tools like Textio, Ongig, or a plain prompt in ChatGPT or Claude can flag passive voice, complex sentences, gendered language, and jargon. The risk is over-relying on a vendor model without calibrating it against your specific roles. One finance team found a readability tool flagging "attention to detail" as a bias signal, but their best hires consistently named precision as core to the work. Calibrate every tool against your own hire quality data. Use AI suggestions as a first-pass review, not a final arbiter. Track apply rates before and after changes so you have evidence, not opinions. See AI bias audit for the audit framework.
What is the two-minute scan test for a JD?
Print or paste your JD and read only the headings, first sentence of each paragraph, and bullet points. If a qualified candidate cannot answer "what does this person do, who do they need to be, and why would they want this job" after two minutes of scanning, the JD fails. Most fail because the first three sections cover the company, the department history, and a culture blurb before ever describing the work. Move the day-to-day tasks and the most differentiating benefit to the top half. Keep bullets to three to five words. Remove any line an experienced candidate already assumes.
How should TA teams govern JD templates across hiring managers?
Create a template library in a shared system (Notion, Confluence, or a private GitHub repo) with approved structures for each role family. Document which phrases are blocked, why, and what alternatives are approved. Run new JDs through a readability check before they reach a job board or ATS. Assign a TA ops owner for quarterly template audits. When hiring managers override the template, log the change so you can track which edits correlate with weaker applicant quality or longer time to fill. Link templates to your ATS so governance lives where recruiters already work.
Where can a recruiter practice JD improvement with AI?
The AI in recruiting workshop at AI with Michal covers JD writing as part of the intake-to-brief workflow, including how to use prompt chains to draft, review, and improve JDs in one session. The Starting with AI: the foundations in recruiting course covers structured prompt use for JD generation. Bring a real JD from a role you own, run it through a readability check live, and use workshop prompts to produce a cleaner version you can actually post. See intake to JD AI for the full AI-assisted workflow.