Job description generation turns a short intake into a structured, on-brand job post in your company voice, with inclusive, bias-aware language and consistent formatting across every role. A hiring manager reviews and posts. Live in one to two weeks.
People & HR
Quick answer
Job description generation turns a short intake, the role, level, must-haves, and team, into a structured job post written in your company voice, with inclusive, bias-aware language and consistent formatting across every role. A hiring manager owns the requirements and reviews before anything posts. It drafts, a person approves. Most teams are live in one to two weeks.
The problem
Job posts get written from scratch every time, or worse, copied from a two-year-old posting nobody remembers approving. One manager's version is a wall of text, another's is three bullet points. The tone drifts, the must-haves are half-guessed, and the "requirements" list quietly grows every year because nobody wants to be the one who left something out. Because there is no shared starting point, each open role is its own small writing project, squeezed in between everything else the hiring manager is already doing.
Put a number on it. LinkedIn's analysis of job posts found that short listings, roughly 300 words or fewer, get candidates to apply about 8.4 percent more often than average, while medium-length posts of 301 to 600 words run about 3.4 percent below average, partly because most people now read job posts on their phones. So the long, dense description a manager sweats over can actively cost applications. And the drafting itself is not free. Writing one job post from scratch runs a hiring manager a couple of hours, and a 40-person firm opening ten roles a year spends around twenty hours a year just on first drafts. (The two-hours-per-role figure is a modeled estimate, not a client number. The application-rate figures are LinkedIn's.)
The hours are not even the real cost. The real cost is the wrong people applying and the right people skipping, because the post described a role that does not quite exist or listed requirements that do not really matter. Research by Gaucher, Friesen, and Kay (2011) found that masculine-coded wording in job ads makes roles less appealing to women and narrows the pool before anyone even reviews a resume. None of that shows up on a timesheet, and all of it makes hiring slower and worse.
How the automation works
1Step 1
Fill a short intake.
The hiring manager answers a few questions: the role, level, the real must-haves, the team it sits in, location, and anything specific to this hire. Two minutes, not two hours.
2Step 2
It drafts in your voice.
The system writes a full, structured post using your past approved job posts and brand voice as the pattern, in the same format every role follows, and it checks the wording for inclusive, bias-aware language as it goes.
3Step 3
A hiring manager reviews and posts.
The draft lands ready to edit. The manager confirms the requirements, adjusts anything specific to this role, and pushes it into your applicant tracking system. Nothing publishes without a human sign-off.
The pieces are proven: a language model that writes to a house style, your own posts as voice samples, a fixed structure template, an inclusive-language check, and a connector into your ATS. The real work is the wiring. The hard part is capturing the true must-haves so the post reflects the actual role and seniority, because a generic AI post that invents requirements or gets the level wrong wastes everyone's time and can screen out good people. That is why a person owns the requirements and reviews every draft. The value here is consistency and inclusive wording, not inventing the role. That is what gets set up, tested, and handed over during implementation.
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A 40-person B2B software company opening a mid-level role.
One hiring manager who writes job posts between everything else on their plate.
Before
Each manager writes posts from scratch or copies an old one, so tone and structure change from role to role.
One post takes about 2 hours between drafting, formatting, and second-guessing the wording.
Nobody checks for phrasing that quietly narrows who applies, so it slips through unnoticed.
After
A two-minute intake produces an on-brand draft in the same structure every role uses.
The manager reviews, adjusts the must-haves, and posts in about 15 minutes.
The draft flags masculine-coded or exclusionary wording before the role goes live.
Net effect: drafting drops from roughly 2 hours to about 15 minutes per role, typical, and every post reads like it came from the same company instead of five different people. The bigger win is fewer mis-targeted applicants, because the requirements are accurate and the wording does not push good candidates away before they apply.
Typical impact
~2 hrs to ~15 minto draft and format one job post
1 to 2 weeksfrom kickoff to your first live drafts
One voiceacross every role, whoever opens it
Typical ranges for this pattern, not client claims. Your numbers get modeled in the audit.
Plus most tools with an API. The audit maps your exact stack.
Who this fits
You open roles often enough that slow drafting and inconsistent posts are a real drag
10 or more employees, with more than one person writing job descriptions
You care that posts read on-brand and use inclusive, bias-aware language
A hiring manager will own the requirements and review each draft before it posts
Frequently asked questions
Job description generation is an automation that turns a short intake, the role, level, must-have skills, and team context, into a complete, structured job post written in your company voice. It uses your past approved postings and brand guidelines so every role reads consistently, and it applies inclusive, bias-aware language by default. It does the first draft a hiring manager would otherwise write from scratch, then hands it over for review. The point is not to invent the role. It is to get an accurate, on-brand draft in front of the hiring manager in minutes instead of hours, ready to edit and post.
A static template gives you the same skeleton every time, but the manager still fills in and words every section by hand, and templates drift as people copy old posts. A generic ChatGPT prompt writes fast but does not know your voice, your past roles, or your standards, so it invents requirements and reads like every other AI post. This is set up on your actual postings and brand voice, checks wording for inclusivity, and pushes the draft into the tools your team already uses. You own the requirements and the final edit. It handles the blank-page part and keeps every role consistent.
It helps, but a person still reviews. Research by Gaucher, Friesen, and Kay (2011) showed that masculine-coded wording in job ads, words like competitive and dominant, makes roles less appealing to women and can shrink your applicant pool. The system is set up to favor neutral, inclusive phrasing and to flag wording that tends to narrow who applies. It does not remove human judgment. It cannot know your full context, and inclusive language keeps evolving, so a hiring manager reviews every draft. The value is a consistent, bias-aware starting point, not a guarantee, and the person posting still owns the final wording.
Yes, always. This drafts. It does not auto-post. Every job description goes to the hiring manager to check the requirements, adjust the seniority and must-haves, and edit the wording before anything is published. The hiring manager owns what the role actually is, because only they know the real must-haves and the team context. The automation removes the blank page and the formatting, and it keeps the voice consistent, but the person opening the role is the one who approves and posts. Nothing reaches a candidate without a human sign-off.
On the drafting and delivery side, it connects to your applicant tracking system, such as Greenhouse, Ashby, Lever, or Workable, so approved posts push straight into your hiring pipeline. It also works with Notion, Google Docs, and Slack for intake and review, and with your CRM such as Attio. It can pull from your existing job posts and brand guidelines wherever those live. Most tools with an API can be added. The audit maps your exact stack and connects the drafting step to wherever your team already opens and manages roles.
Usually one to two weeks. The first days go to gathering your best existing job posts, your brand voice, and the structure you want every role to follow, then setting up the inclusive-language checks. After that the drafting runs against a few real roles so the output gets tuned to your voice and standards before anyone relies on it. You are producing usable drafts quickly, and the tuning window is what makes those drafts read like your company wrote them rather than a generic tool.
Two parts. Tooling runs as a modest monthly cost for the model usage and any connectors, typically a small per-user or per-role range that scales with how much you hire. Implementation is a fixed scope, quoted once the audit maps your voice, templates, tools, and review flow, so you are pricing a defined build rather than an open-ended retainer. The audit itself is where the scope and price get set against every other opportunity in your business, so you are not guessing at effort up front.
