Somewhere right now, a small business is using ChatGPT to write a job posting that another company’s AI will screen for keyword matches — and neither AI is checking whether the posting itself is legally defensible.

That’s the quiet joke sitting underneath the entire AI-job-description-tool market. Vendors sell “bias-free” language generation as the premium feature. Meanwhile the actual risk in most small-business hiring isn’t which large language model drafted the posting — it’s that nobody read it before it went live.

Quick answer: For most small businesses posting fewer than about 10 roles a month, with one person reading each posting before it goes live, ChatGPT or Grammarly’s free job description generator plus a short manual bias check is enough. Switching to a dedicated tool — Textio, Ongig, or Datapeople — earns its price once a business is posting across multiple states, multiple non-HR managers are drafting without oversight, or an audit trail matters more than marginally “smarter” bias detection.

The rest of this comes down to what each option actually catches, what it costs, and what the law actually says about who’s liable when a job posting goes wrong.

The Real Question Isn’t Which AI Writes Better — It’s Who’s Checking the Output

Every job-description tool on the market — free or paid — generates text from a large language model. Strip away the branding and the underlying mechanism is the same everywhere. The differentiator was never the writing. It’s what happens after the draft appears on screen.

Katrina Kibben of Three Ears Media made this point directly in “The Future Of Work: Why AI Can’t Write Job Descriptions (Yet)” (katrinakibben.com, August 2023): AI skips the intake conversation entirely. It doesn’t ask a hiring manager why the role exists, what actually matters about it, or what’s negotiable. It just rewords whatever wish list gets typed into the prompt box.

That’s a real limitation, and it’s worth sitting with — but it’s not disqualifying. Job description drafting is an administrative task, not a hiring decision. That distinction matters more than most vendor marketing lets on: a first-draft posting that gets edited by a human before publishing is a fundamentally different risk profile than an AI system screening or ranking live applicants.

Small businesses already automate plenty of adjacent admin work the same way — fielding routine employee questions with AI HR chatbots for small business, or handling first-pass candidate screening conversations with AI recruiting chatbots. The pattern holds across all of it: fine for admin tasks with a human backstop, risky the moment it starts making decisions unsupervised.

Vendors selling dedicated JD tools price their products around “scientific” bias reduction, as if paying more buys meaningfully better language. In practice, a free tool plus an attentive human editor catches most of the obvious problems — gendered phrasing, age-coded requirements, unnecessary jargon. The real gap between a bad posting and a defensible one isn’t the brand of AI that wrote the first draft. It’s whether a human review step exists at all.

ChatGPT vs. Grammarly vs. Datapeople vs. Textio vs. Ongig: Quick Comparison

The same question small businesses ask about job descriptions applies to other AI-generated HR documents. Whether an AI employee handbook generator worth it for a small business is a comparable calculus: useful for a first draft, still needs a human who understands the business to sign off before it becomes policy.

Worth noting on its own: none of the three dedicated vendors publish list pricing anywhere on their sites. Every quote requires a sales call. That opacity is itself a signal. These products are built and priced for teams posting dozens of roles a month, not a five-person shop hiring twice a year.

Free Tools: What ChatGPT and Grammarly’s JD Generator Actually Catch

Grammarly’s job description generator is free, requires no signup, and integrates with Google Docs and Word. It’s built to check tone, formality, and clarity — not to score or flag exclusionary language. It will make a posting read more polished. It will not tell anyone the posting skews masculine-coded or age-biased.

ChatGPT, at $20/month for Plus (or free with usage limits), has zero built-in bias detection unless a user explicitly asks for it. And even when asked, its output carries a documented limitation worth taking seriously: Scientific American’s reporting on “ChatGPT Replicates Gender Bias in Recommendation Letters” and multiple arXiv studies on gender bias in large language models have found that these models reproduce gender-coded patterns learned from training data — associating “manager” with male-coded language and using more communal, relationship-focused phrasing for female-coded names. This is a real, documented limitation, not a hypothetical one, and it’s exactly the kind of language that shows up unchecked in a job posting nobody reviewed.

That’s precisely why dedicated AI bias audit software exists as a category — tools built specifically to catch this pattern before a posting goes live, rather than relying on a general-purpose chatbot to police its own output.

Practitioners on Reddit are already using ChatGPT this way — as a starting point, not a finished product. A poster in r/humanresources described the workflow bluntly: “Job descriptions. Rough draft from AI, then I edit… A 90-minute task becomes 20.” A recruiter in r/recruiting laid out a similar pattern: “I use it to ask technical screening questions… write out job descriptions if the hiring manager doesn’t have one.” Another recruiter in the same subreddit added the caveat that makes the whole approach work or fail: “those are always very generic, so if you use it for the base make sure to edit.”

That last line is the entire argument for free tools in one sentence. They’re fine at low volume, provided a human actually edits the output before it ships. Skip that step and the tool’s limitations become the company’s liability.

Dedicated Tools: What You’re Actually Paying For (Datapeople, Textio, Ongig)

Textio’s core feature is its real-time Textio Score, which flags gendered language, age-coded phrasing, tone problems, and reading difficulty as a posting is being typed. Textio’s own marketing cites a vendor-reported statistic: postings that score highly see 25% more qualified interviews, 23% more women applicants, and 17% faster hiring. That’s a vendor-reported figure from Textio’s own materials, not an independently verified study, and it should be read that way.

Skepticism about the underlying methodology isn’t confined to competitors. Reviewers on ProductHunt have pushed back directly on the scoring logic. One asked the question that matters most: “How do you decide what phrases are gender biased? … I’d love to see the science behind this.” Another reviewer described testing the score’s sensitivity and finding it oddly easy to game: “as soon as I add Textio’s canned ‘equal opportunity statement’… goes to ‘very good’ … Just adding a disclaimer adds 30 points? Calling shenanigans.”

Ongig takes a different angle, claiming it flags more than 10,000 exclusionary phrases across gender, race, age, disability, mental health, and LGBTQ+ language — a figure that comes from Ongig’s own marketing and should be labeled as a vendor claim rather than an independent benchmark. Its real value proposition is less about detecting anything a free tool can’t and more about consistency: bulk editing across dozens of postings at once, plus ATS import so an entire req library can be audited in one pass.

Datapeople follows a similar model — real-time compliance and bias flagging built directly into the drafting flow, aimed squarely at talent acquisition leaders who need to track diversity metrics across a large volume of simultaneous hires.

The honest read on all three: bulk consistency and an audit trail across many job postings are genuinely useful for the right buyer. The “our AI is more scientifically unbiased” pitch is the weakest part of the sales case, and reviewers testing these tools in public have been the ones to poke holes in it — not competitors with something to sell against it.

The Actual Legal Risk (And It Has Nothing to Do With Which Tool You Pick)

The EEOC’s May 2023 guidance, “Assessing Adverse Impact in Software, Algorithms, and AI Used in Employment Selection Procedures” (summarized by Fisher Phillips and the ABA), established a principle that outlasts any single tool comparison: AI-assisted hiring tools can trigger Title VII adverse-impact liability, and that liability sits with the employer — not the vendor — regardless of what the vendor’s contract says.

In 2025, the EEOC removed its AI technical-assistance documents from public guidance, and an Executive Order directed federal agencies to de-prioritize disparate-impact enforcement. According to Fisher Phillips’ analysis, that shift changes enforcement posture but not the underlying law: Title VII, the ADEA, and the ADA still apply in full, and private plaintiffs can still sue directly, independent of what the EEOC chooses to prioritize.

The clearest illustration of where this trend leads is Mobley v. Workday. CNN Business reported in May 2025 that a federal judge certified a collective action alleging Workday’s AI screening tool filtered out applicants over 40. It’s worth being precise about what this case is and isn’t: it’s an AI-screening case — a tool making pass/fail decisions on live applicants — not a job-description-writing case. It’s cited here only because it shows where courts are willing to let liability claims proceed when AI touches the hiring pipeline, and job descriptions are the very first step in that pipeline.

A poster in r/recruiting summed up the discovery risk in plain terms: “Please don’t use ChatGPT for your HR work. If during a discovery process … it comes out you did this on your own, you will get hung out to dry.” Elsewhere in r/humanresources, one HR professional described testing an AI-generated absence policy and catching it missing key legal points — the kind of gap a small-business owner without HR training might not notice until it’s already been published or, worse, enforced. Businesses hiring in the EU also carry exposure under Regulation 2024/1689, which adds its own compliance layer on top of U.S. law.

The practical translation: a badly written AI job description is a far cheaper problem to fix than a discriminatory AI screening tool — but “cheap to fix” doesn’t mean invisible. It’s still discoverable, still attributable, and still the employer’s problem. That’s the same underlying lesson as the broader AI hiring tool lawsuit and compliance risk pattern playing out across the industry right now: a documented human review checklist matters more than which AI generated the first draft.

So When Should You Switch From ChatGPT? (Our Take)

Stay on free tools — ChatGPT, Grammarly, and a manual review checklist — if a business fits three conditions: fewer than about 10 open roles a month, one specific person reads every posting before it goes live, and hiring happens in a single state.

Consider a dedicated tool once any of these show up instead: postings running across multiple states or countries with different compliance requirements, multiple non-HR hiring managers drafting postings without centralized review, or a large enough applicant pool in a regulated industry that an audit trail becomes worth having on its own merits — separate from whatever the tool’s bias score claims to prove.

What shouldn’t drive the decision is a vendor’s claim that their AI is “more scientifically” bias-free. That’s the weakest argument in the entire market, and it’s the one reviewers have been most willing to publicly dismantle. The highest-leverage, lowest-cost fix available to any small business isn’t a $13,000-a-year subscription — it’s a written five-point human review checklist, used consistently, with a record that someone actually followed it.

Frequently Asked Questions

Can ChatGPT or Grammarly’s free job description generator catch biased or exclusionary language, or does it just sound polished?

Neither is built to score or flag bias. Grammarly checks tone, clarity, and formality. ChatGPT will reproduce documented gender-coded patterns from its training data unless explicitly prompted to check for bias — and even then, it’s not a dedicated detection system. Both can produce a polished-sounding draft that still contains language a dedicated bias tool or a careful human editor would flag.

How much do Textio and Ongig actually cost, and is that justified for under 10 roles a month?

Textio doesn’t publish list pricing; third-party estimates put it around $10,000 to $15,000 per year. Ongig’s own published tiers run from about $4,900 a year (Lite) to $13,900–$14,900 a year (Professional) and $39,900 and up (Enterprise). For a business posting fewer than 10 roles a month, that cost is hard to justify against a free-tool-plus-review-checklist workflow that catches most of the same problems.

Do AI-generated job descriptions really increase qualified or diverse applications, or is that vendor-reported data?

Textio’s cited figures — 25% more qualified interviews, 23% more women applicants, 17% faster hiring — come from Textio’s own marketing and should be read as vendor-reported, not independently verified. Reviewers on ProductHunt have specifically questioned the methodology behind Textio’s scoring, including how easily the score can be raised with generic disclaimer language rather than substantive edits.

What’s the legal/compliance risk of posting an AI-written job description without human review?

The EEOC’s 2023 guidance established that liability for AI-assisted hiring tools rests with the employer, not the vendor, and that hasn’t changed even as enforcement priorities shifted in 2025 — Title VII, the ADEA, and the ADA all still apply, and private plaintiffs can still sue. An unreviewed AI job description is discoverable in litigation, and community reports describe AI-generated policy documents missing legal points a trained HR professional would catch.

At what hiring volume does a dedicated JD tool actually pay for itself over free tools?

Roughly once a business is posting across multiple states or countries, has multiple non-HR managers drafting postings without centralized oversight, or needs a documented audit trail across a large applicant pool in a regulated industry. Below that threshold, the value dedicated tools add over a free tool plus a human reviewer is marginal relative to the price.

The Verdict: Buy the Process, Not the Subscription

For most small businesses posting under 10 roles a month with one person reading every posting before it goes live, free tools are enough. The money set aside for Textio or Ongig is better spent building an actual review process first — because none of these tools, free or paid, replace the judgment of someone who reads the posting before it publishes.

Before the next job posting goes out, run it through a written five-point human bias and compliance checklist, and keep a record that someone did it. That single habit does more for legal defensibility than any AI bias score.

The AI didn’t create the discrimination risk sitting in your job postings — it just made it fast enough to publish before anyone thought to check.