As Jeff Arnold, I’ve seen firsthand how leveraging automation and AI can transform HR functions, especially in talent acquisition. One of the most impactful, yet often overlooked, areas is optimizing how we attract top-tier talent right from the first interaction: the job description. Generic JDs lead to generic applicants. That’s why I’ve developed this guide to show you how to move beyond guesswork and systematically improve your applicant quality.

This guide will walk you through a practical, step-by-step process for A/B testing your AI-generated job description prompts. The objective isn’t just to get more applications, but to attract *better* applications—candidates who are truly a fit for the role and your organizational culture. It’s about empowering your recruiting teams with data-driven insights to refine their outreach and build stronger talent pipelines, ultimately saving time and resources.

1. Define Your “Quality Applicant” Metrics & Baseline

Before you can measure improvement, you need to understand what “improvement” looks like for your organization. This first step is critical. What makes a “quality applicant” in your context? Is it a higher percentage of candidates passing the initial screening, a better interview-to-offer ratio, or a reduction in time-to-hire for successful candidates? Clearly define 2-3 quantifiable metrics. For instance, you might track the percentage of applicants who meet all essential criteria, the average score on a pre-screening assessment, or the proportion of candidates who advance to the second interview stage. Simultaneously, establish a baseline for these metrics using your current job description strategies. This will give you a benchmark against which to compare the performance of your new, AI-generated variations. Without this clarity, your A/B test results will lack actionable insights.

2. Engineer Your Initial Job Description Prompts

Now for the fun part: leveraging AI! I’ve found that the quality of your prompt directly correlates to the quality of the JD output. Start by crafting two distinct prompts (A and B) designed to generate slightly different job descriptions for the *same* role. For example, Prompt A might emphasize career growth opportunities and innovative projects, while Prompt B could highlight company culture, work-life balance, and immediate impact. Be specific in your prompts: include details about the role, required skills, preferred experience, company values, and even the desired tone (e.g., “energetic and ambitious,” “collaborative and inclusive”). Use tools like ChatGPT, specialized HR AI platforms, or even internal generative AI models to create these variations. The goal is to produce two genuinely different but equally compelling job descriptions that you believe might resonate differently with your target audience.

3. Implement Your A/B Test Framework

With your two distinct job descriptions (JD-A and JD-B) ready, it’s time to set up your A/B test. The key here is to ensure an equitable distribution of exposure to both versions. If your Applicant Tracking System (ATS) has A/B testing capabilities, use them. If not, you might consider posting JD-A on one set of job boards (or for a specific duration) and JD-B on another, or alternating between them if using a single platform. Ensure that all other variables remain constant—the job title, the posting location, salary range, and the timeframe for the test should be identical. Crucially, establish a clear method for tracking applications received for each specific JD variant. This might involve unique tracking codes, separate application links, or specific questions in your application form. Consistency in setup is paramount for valid results.

4. Execute the Test and Collect Data Consistently

With your framework in place, launch both job descriptions simultaneously or sequentially, ensuring each receives comparable visibility. This phase is about disciplined execution and meticulous data collection. Let the test run for a predetermined period (e.g., 2-4 weeks) or until you’ve gathered a statistically significant number of applications for both JD-A and JD-B, based on your typical applicant volume. During this time, actively monitor the pipeline for each variant. Track not just the sheer volume of applications, but crucially, the progress of candidates from each JD through your hiring funnel. Are applicants from JD-A more likely to pass the initial screening? Do candidates from JD-B have higher interview-to-offer rates? Consistency in data entry and tracking across all stages of the recruitment process is vital to ensure you have reliable insights for the next step.

5. Analyze, Optimize, and Scale Your Winning Prompts

Once your test period concludes and you’ve collected sufficient data, it’s time to crunch the numbers. Compare the performance of JD-A versus JD-B against the “quality applicant” metrics you defined in Step 1. Which job description variant consistently attracted candidates who were better qualified, more engaged, or moved further down the hiring funnel? Identify the specific elements within the winning prompt that likely contributed to its success—was it the focus on culture, the explicit mention of growth, or a particular phrasing of responsibilities? Use these insights to refine your best-performing prompt, or create a new “challenger” prompt to test against your current winner in a subsequent iteration. Finally, scale your learnings: integrate the successful prompt engineering strategies into your standard operating procedures for job description creation across your recruitment team, maximizing your ROI on this automation effort.

If you’re looking for a speaker who doesn’t just talk theory but shows what’s actually working inside HR today, I’d love to be part of your event. I’m available for keynotes, workshops, breakout sessions, panel discussions, and virtual webinars or masterclasses. Contact me today!