Fix Poor Job Descriptions in HR Recruiting with Gemini AI

Standardize Your Role Inputs Before You Generate Anything

Before switching on Gemini, create a simple, standardized intake form for each role. At minimum, capture: role mission (3–4 sentences), top 5 responsibilities, 5–7 must-have skills, 3–5 nice-to-have skills, reporting line, team structure, work model (remote/hybrid/on-site), location, and compensation range or band.

Feed this structure directly into Gemini instead of unstructured email threads. This can be done via a lightweight internal form or an ATS integration that collects this data from hiring managers. The more structured the input, the more accurate and reusable the AI-generated job descriptions will be across languages and channels.

Example prompt to Gemini (system + user combined for illustration):

You are an HR content specialist. Write a clear, inclusive job description.
Follow these rules:
- Use our employer branding tone: optimistic, direct, jargon-free
- Clearly separate role mission, responsibilities, requirements, and benefits
- Keep requirements realistic and avoid inflated wishlists
- Use gender-neutral and inclusive language

Role information:
- Title: Senior Data Analyst
- Mission: <paste role mission from form>
- Top responsibilities: <paste bullets>
- Must-have skills: <paste bullets>
- Nice-to-have skills: <paste bullets>
- Reporting to: Head of Analytics
- Work model: Hybrid, 3 days on-site in Berlin
- Salary band: €75,000–€90,000 depending on experience

Generate:
1) Short job ad for LinkedIn (max 1,000 characters)
2) Full job description for our career page
3) 3 title variations optimized for search.

This approach turns scattered role notes into a repeatable pipeline that Gemini can reliably work with.

Create Reusable Prompt Templates for Different Job Families

Gemini performs best when you design dedicated prompt templates for different job families: tech, sales, operations, corporate functions, etc. Each family has typical structures, jargon to avoid, and channels that matter. Encode those specifics into tailored prompts instead of using one generic template for all roles.

For example, technical roles might need more detail on tech stack and problem space, while customer-facing roles need stronger emphasis on communication and impact. Store these templates centrally (e.g., in your ATS, internal wiki, or a small internal tool) so recruiters can trigger the right Gemini prompt with minimal manual editing.

Example Gemini prompt template for engineering roles:

You are creating a job description for a software engineering role.

Guidelines:
- Speak directly to experienced engineers
- Emphasize impact, autonomy, and technical challenges
- List tech stack clearly in one section
- Avoid generic phrases like "fast-paced environment"

Insert:
- Role mission:
- Product context:
- Tech stack:
- Team setup:
- Growth and learning opportunities:

Output:
- SEO-optimized job title
- 3-sentence role pitch
- Responsibilities (5–7 bullets)
- Requirements, separated into must-have and nice-to-have
- Benefits section aligned with our EVP.

Over time, refine these templates based on which postings generate the best candidate pipelines.

Automate Multilingual Job Descriptions with Built-In Consistency Checks

For organizations hiring across markets, multilingual job descriptions are another area where Gemini can save significant time. Implement a workflow where the “source of truth” is a single, well-reviewed base description (often English or the company’s main language), and Gemini generates localized versions in other languages.

Use prompts that ask Gemini to keep role requirements identical while adapting phrasing to local market expectations. Then, add a second Gemini check: have it compare two language versions and highlight any inconsistencies in requirements, benefits, or seniority level. This reduces the risk of different markets advertising subtly different roles.

Example configuration:
1) Recruiter approves the master job description in English.
2) HR triggers Gemini with:
"Translate and localize this job description into German and French.
Keep requirements and seniority identical. Adapt benefits wording to local norms."
3) Run a comparison prompt:
"Compare the English and German versions. List any differences in:
- Requirements
- Seniority
- Compensation
- Working model"
4) HR reviews flagged differences before publishing.

This sequence ensures speed without losing control over role consistency across countries.

Integrate Gemini Directly into Your ATS or Career Site Backend

To avoid copy-paste chaos, integrate Gemini via API into your ATS or career site backend. In a typical implementation, recruiters click “Create job description” in the ATS, which triggers Gemini with the structured role data already stored in the system. The generated draft is saved directly in the requisition record for review and editing.

Work with IT and vendors to define where data is stored, which fields are sent to Gemini, and how to log AI usage for compliance. Reruption often helps clients build a simple mid-layer service that handles Gemini prompts, versioning, and auditing, without locking the organization into one ATS vendor or UI.

Example technical sequence:
1) Recruiter creates a new requisition in the ATS and fills in role fields.
2) Recruiter clicks "Generate JD".
3) ATS sends a JSON payload with role data to an internal service.
4) The service calls Gemini with:
   - System prompt (brand, DEI rules, structure)
   - Role data (title, mission, skills, etc.)
5) Gemini returns the draft. The service stores it with version = 1.
6) ATS displays the draft for editing and approval.
7) Final approved version is flagged as "Published" and pushed to the career site.

This keeps the user experience simple while making the AI integration maintainable and auditable.

Embed Bias and Complexity Checks into the Workflow

Even with good prompts, job descriptions can accidentally become long, complex, or subtly exclusive. Use Gemini as a second-pass reviewer: once a recruiter finalizes a draft, trigger another prompt that evaluates the text for inclusive language, readability, and unnecessary requirements.

Ask Gemini to highlight potential issues instead of silently changing the content. This gives HR visibility and control while still benefiting from AI support. Over time, capture recurring issues (e.g., common gender-coded phrases or inflated degree requirements) and bake them into your base prompts.

Example review prompt:

You are an inclusive recruiting expert.
Review the following job description and provide:
1) A readability score and suggestions to simplify language
2) Any phrases that may discourage diverse candidates (e.g., gender-coded terms)
3) Any requirements that seem unnecessary or could be reframed as "nice-to-have"
4) A revised version that implements your recommendations, while preserving
   the core responsibilities and requirements.

Text:
<paste final draft>

Expected outcome: cleaner, more inclusive job descriptions that widen the talent pool without sacrificing relevance.

Measure Impact with Clear Before/After Metrics

To demonstrate ROI, define concrete metrics before rolling out Gemini. Track indicators such as: time from requisition creation to published job, percentage of applicants meeting must-have criteria, number of CVs screened per hire, interview-to-offer ratio, and diversity of the applicant pool (where legally and ethically possible).

Compare a 3–6 month baseline period with the first months after implementing Gemini-generated job descriptions. In many environments, HR teams can realistically expect: 30–50% reduction in time to publish a job, 15–30% increase in the share of relevant applicants, and a noticeable reduction in back-and-forth editing with hiring managers. These concrete improvements make it easier to secure further investment in AI for HR.