Feedback-Aware Prompt Optimization Framework for Generating Job Postings

AbstractJob postings are critical for recruitment, yet large enterprises struggle with standardization and consistency, requiring significant time from hiring managers and recruiters. We present a feedback-aware prompt optimization framework that automates high-quality job posting generation through iterative human-in-the-loop refinement. Our system integrates multiple data sources: job metadata, competencies, organization’s compliance guidelines, and organization brand statement, while incorporating human feedback to continuously improve prompt quality through multi-LLM validation. We evaluated our approach using LLM-as-a-judge on 1,056 job postings and human evaluation on a smaller subset across three dimensions: Standardization, Compliance, and User Perception. Our results demonstrate high compliance rates and strong satisfaction scores in both automated and human evaluation, validating the effectiveness of our feedback-aware approach for enterprise job posting generation.