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        <title>Feedback-Aware Prompt Optimization Framework for Generating Job Postings</title>
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        <namePart type="given">Suraj</namePart>
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            <title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)</title>
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            <namePart type="given">Yevgen</namePart>
            <namePart type="family">Matusevych</namePart>
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    <abstract>Job 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.</abstract>
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%0 Conference Proceedings
%T Feedback-Aware Prompt Optimization Framework for Generating Job Postings
%A Maharjan, Suraj
%A Yessenalina, Ainur
%A Sengamedu, Srinivasan H.
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F maharjan-etal-2026-feedback
%X Job 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.
%U https://aclanthology.org/2026.eacl-industry.35/
%P 467-474

Markdown (Informal)

[Feedback-Aware Prompt Optimization Framework for Generating Job Postings](https://aclanthology.org/2026.eacl-industry.35/) (Maharjan et al., EACL 2026)

• Feedback-Aware Prompt Optimization Framework for Generating Job Postings (Maharjan et al., EACL 2026)

ACL

• Suraj Maharjan, Ainur Yessenalina, and Srinivasan H. Sengamedu. 2026. Feedback-Aware Prompt Optimization Framework for Generating Job Postings. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 467–474, Rabat, Morocco. Association for Computational Linguistics.