Rewrite ChatGPT-drafted job descriptions before they hit LinkedIn Jobs, Greenhouse, Lever, or Workday. Sentence-level highlights flag the passionate openers, rockstar vocabulary, inflated year counts, and uniform eight-bullet responsibilities that get filtered out by senior candidates and surfaced as inadvertent bias by EEOC reviewers. Light mode preserves named tools, salary bands, and reporting lines so candidate-side ATS keyword-fit tools still match. Free to try. No card.
A senior engineer opens LinkedIn Jobs, sets a filter, gets back 80 results, and has roughly twenty minutes to triage them. The first signal they screen on is the opening sentence and the responsibilities block. Three patterns collapse the funnel before the salary band is even visible, and each one is a ChatGPT default that the AI rewriter surfaces sentence by sentence.
Job descriptions are an especially exposed format for generative-AI review because they sit at the top of a hiring funnel where the audience is short on time, long on options, and trained by years of LinkedIn scrolling to recognise template-shaped postings inside three seconds. A ChatGPT-drafted JD will look right at a glance, but the opener, the parallel responsibilities, and the inflated year counts together signal to a strong candidate that the hiring manager did not spend much time on the role. The realistic 2026 workflow uses AI assistance for the first draft and rewrites the published version before it goes live on the ATS.
A passionate, results-driven opener is the single most-recognised AI JD tell. Strong candidates have seen the construction enough times that they close the tab without reading the rest. The fix is to open with a concrete problem the role exists to solve, anchored to a specific number or system. "Our analytics pipeline ingests forty million events a day and we need someone to own its next rewrite" is a working opener; "Are you a passionate, results-driven data engineer?" is not.
ChatGPT defaults to a parallel-structure responsibilities list with seven or eight bullets, each starting with a verb, each carrying roughly the same syllable count. The list scans cleanly and fills space, but strong candidates read parallel structure as a template fingerprint and assume the hiring manager has not thought concretely about what the role will do. The healthier pattern is three to five responsibilities with mixed length, including at least one two-sentence item that describes a first-quarter project in detail.
ChatGPT's training data is heavy on JDs that inflate year requirements, so it tends to draft "five plus years of modern frontend frameworks" when the role actually needs two years of React. Strong candidates with the right skills self-deselect, and the inflated requirement disproportionately deters underrepresented applicants (a documented EEOC bias-vector). The AI rewriter flags inflated year language so you can downgrade or replace it with a task-shaped signal.
"Competitive salary, comprehensive benefits, fast-paced collaborative environment" appears nearly verbatim on a third of careers pages a candidate browses. The line carries no signal and burns thirty words. Candidates have learned that specific commitments correlate with real policies and generic enthusiasm correlates with whatever HR negotiated last quarter. Replacing the boilerplate with one concrete policy (sixteen weeks of parental leave at 100 percent, posted salary band, learning budget figure) lifts trust without adding length.
A title line and a benefits paragraph are not the same animal. Each JD section has its own register, its own paraphrase density, and its own false-positive risk. Read the score in the context of the section rather than chasing one number across the whole posting.
Short-form, twelve to twenty-five words combined. Chunk size sits below the classifier's reliable band, so the title and one-liner score noisily on their own. The realistic move is to scan the title and summary together with the company-intro paragraph so the model has enough signal. Healthy titles read as a real role inside a real team (Senior Payments Engineer, Disputes pod, reporting to the VP Engineering) and score 70 to 85. Generic titles (Rockstar Full-Stack Ninja) score in the teens.
Forty to one hundred words describing who the role reports to, what team it joins, and what the first-quarter scope looks like. This is where ChatGPT slips hardest into passionate, results-driven openers and "fast-paced, dynamic environment" filler. Scan the summary alongside the responsibilities block so chunk size is large enough for a stable read. The healthy pattern is two to three sentences with one concrete project named.
The highest-AI-risk section in most JDs. ChatGPT defaults to a uniform parallel-structure list (Lead cross-functional initiatives, Drive strategic alignment, Champion data-driven decision making) that reads as template even to a generalist recruiter. Scan the responsibilities block on its own first; if every bullet starts with a verb of the same cadence the score drops below 30. The fix is varied sentence length with at least one two-sentence bullet describing an actual first-quarter project.
The section candidate-side ATS tools (Teal, JobScan, Resume Worded, Huntr) parse hardest because they extract named skills, tools, and year counts to score a resume against the role. Light mode preserves Python, AWS, Figma, three years, salary band, and reporting line verbatim, which keeps candidate-side keyword-fit scoring intact. Audit this section after every AI rewriter pass to confirm the hard skills your hiring rubric actually uses are still listed.
Where generic ChatGPT enthusiasm and templated equal-opportunity paragraphs cluster. The healthy pattern is one or two concrete policy statements (parental leave terms, async-first norms, posted salary bands) plus a short EEOC statement written in your own voice. Generic boilerplate flags hard on detector scores and reads as untrustworthy to candidates from underrepresented groups, who respond more to specific commitments than to templated diversity statements.
Short-form, twenty to fifty words. Tells candidates how to apply, what materials to send, and what the process looks like. Most JDs default to "Apply through our portal" which is fine but adds no signal. A version that names the next step (a 30-minute screen with the hiring manager, then a 90-minute system-design pair) reduces no-show rates and reads more human. Scoring is volatile on this section alone; scan it together with benefits for a stable read.
Pro at $19.99 a month standard, $14.99 a month on yearly, fits in-house hiring managers and recruiters posting five to fifteen roles a quarter. Business at $39.99 a month standard, $29.99 a month on yearly, fits recruiting agencies and RPO teams writing twenty or more JDs a month. Full details on the pricing page.
Billed $89.88/year — Save $30
Billed $179.88/year — Save $60
Billed $359.88/year — Save $120
Yearly billing saves 25%. View full pricing →
AI-generated JDs can introduce inadvertent bias through gendered vocabulary, inflated qualifications, and templated equal-opportunity boilerplate that carries no specific signal. The AI rewriter flags the patterns. The EEOC review and inclusion sign-off still belong with your People team or an external compliance partner. Treat the score as one input alongside Textio, Ongig, or an internal checklist, not as a substitute for them.
Rockstar engineer, marketing ninja, growth wizard, aggressive closer, dominant performer. Each carries documented gendered or exclusionary connotation in hiring-bias research and each reads dated even when it does not. The AI rewriter flags the vocabulary on first scan; the swap is to a specific outcome verb anchored to the actual work (someone who ships production code in their first month, someone who has closed a six-figure enterprise deal end-to-end).
Inflated year requirements disproportionately deter underrepresented applicants who, in self-reporting research, apply only when they meet every requirement listed. A role that genuinely needs two years of React posted as "five plus years of modern frontend frameworks" loses qualified applicants who self-deselect on the year count. The AI rewriter flags inflated year language so you can downgrade to the real requirement or replace it with a task-shaped signal.
A generic equal-opportunity paragraph that appears verbatim on half a candidate's open tabs carries little signal. The compliant-and-specific pattern is one or two sentences naming concrete policies (sixteen weeks of parental leave at 100 percent for any caregiver, posted salary band, accommodation for interview scheduling) plus a short EEOC statement written in your own voice. The legal language can stay; the surrounding context should be specific to your company.
TextSight surfaces template patterns and inadvertent boilerplate. It does not certify a JD as EEOC-compliant, ADA-compliant, or free of disparate impact. For roles in regulated industries, in jurisdictions with pay-transparency laws (New York, Colorado, California, Washington, EU pay-transparency directive), or in any role where you would want a defensible audit trail, route the rewritten version through a dedicated inclusive-language tool and a People-team review before posting.
In 2026 most engaged candidates run the JD through a CV-rewrite or keyword-fit tool (Teal, JobScan, Resume Worded, Huntr) before applying. Those tools extract named skills, tools, and qualifications from the JD and score the candidate's resume against the list. If authenticity drops a keyword the candidate's tool was scoring against, their match score falls and they self-deselect. Light mode is built around preserving that keyword surface.
Named tools (Python, AWS, Snowflake, Figma, Salesforce), role titles, year counts, education requirements, certifications, soft-skill phrases (cross-functional, customer-facing), and named methodologies (agile, OKRs, SCRUM). The output is a percentage fit score plus a list of missing keywords. Candidates run the tool, see they are at 64 percent match with three missing keywords, and either rewrite their resume or skip the application.
Light mode preserves named tools, named methodologies, year counts, salary bands, and role titles verbatim by design. It rewrites the connective tissue (lead-in sentences, parallel-structure padding, generic enthusiasm) without touching the keyword surface. That preserves the candidate-side keyword-fit score, which preserves the application volume from the candidates who screen on it.
Maximum can rewrite a year count, paraphrase a named tool, or collapse a methodology list into a generic phrase. On a JD that pattern can drop a candidate's keyword-fit score by ten to twenty percent and cost you applications from exactly the segment that researches the role before applying. Reserve Maximum for the closing pitch paragraph and the company-intro, where keyword density is already low.
After every AI rewriter pass, paste the rewritten JD into a candidate-side ATS tool (JobScan and Teal both have free tiers) using a synthetic resume that matches your real hiring rubric. Confirm every keyword you would screen on still appears in the JD. The thirty-second check catches the one AI rewriter pass per month that paraphrased a tool name into oblivion.
A real example from a 60-person B2B fintech hiring a Senior Product Manager. The rewritten variant lifted qualified application volume by 41 percent over fourteen days while preserving every hard skill and salary anchor candidate-side ATS tools were scoring against.
"Are you a passionate, results-driven Senior Product Manager who thrives in a fast-paced environment? We are a fast-growing, innovative fintech committed to delivering excellence. You will lead cross-functional initiatives, drive strategic alignment, and champion data-driven decision making. Five plus years of experience in product management required. Excellent communication skills a must. Team player essential. Equal opportunity employer."
"We are a 60-person B2B fintech processing payroll for 8,000 SMB customers. Our Payments PM left last month and we need someone to take over the chargeback dispute flow. In week one you will sit with the support team and watch how disputes get logged today. Week three you will own the redesign brief. We post the salary band (180K to 220K base) and never negotiate down. Required: you have shipped at least one product against a real fraud or compliance constraint. We pay sixteen weeks of parental leave at 100 percent for any caregiver and post bands on every role. We hire on demonstrated work; the loop is two interviews and one paid take-home."
The passionate opener dropped. The boilerplate company description dropped. The parallel three-bullet responsibilities dropped. The inflated five-plus-year requirement dropped and was replaced with a task-shaped signal (you have shipped against fraud or compliance). The templated equal-opportunity paragraph was replaced with one concrete policy (sixteen weeks parental leave, posted bands) that carries actual EEOC signal. Hard skills, salary anchor, and reporting line stayed intact, so candidate-side ATS tools still scored the resume the same way. The score moved 64 points. Qualified applications lifted 41 percent.
More for hiring teams.
Pre-publish detection for JDs plus inbound-resume screening across LinkedIn, Greenhouse, and Lever.
For recruiters →Rewrite ChatGPT-drafted ad copy, landing pages, and email sequences before paid spend.
For marketing →Light, Balanced, and Maximum modes for fixing flagged passages without losing voice or keywords.
Read the guide →Free, Starter, Pro, Business. Yearly billing saves 25%. Hiring-manager to agency tiers.
See pricing →Free to try. No card. Pro at $14.99 a month on yearly for in-house hiring; Business at $29.99 a month on yearly for recruiting agencies and RPO teams.