@inproceedings{agarwal-etal-2025-promptwizard,
    title = "{P}rompt{W}izard: Optimizing Prompts via Task-Aware, Feedback-Driven Self-Evolution",
    author = "Agarwal, Eshaan  and
      Magazine, Raghav  and
      Singh, Joykirat  and
      Dani, Vivek  and
      Ganu, Tanuja  and
      Nambi, Akshay",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.1025/",
    doi = "10.18653/v1/2025.findings-acl.1025",
    pages = "19974--20003",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs. However, manual prompt engineering is both labor-intensive and domain-specific, necessitating the need for automated solutions. We introduce PromptWizard, a novel, fully automated framework for discrete prompt optimization, utilizing a self-evolving, self-adapting mechanism. Through a feedback-driven critique and synthesis process, PromptWizard achieves an effective balance between exploration and exploitation, iteratively refining both prompt instructions and in-context examples to generate human-readable, task-specific prompts. This guided approach systematically improves prompt quality, resulting in superior performance across 45 tasks. PromptWizard excels even with limited training data, smaller LLMs, and various LLM architectures. Additionally, our cost analysis reveals a substantial reduction in API calls, token usage, and overall cost, demonstrating PromptWizard{'}s efficiency, scalability, and advantages over existing prompt optimization strategies."
}