@inproceedings{al-shaibani-etal-2026-promptlab,
    title = "{P}rompt{L}ab: A Collaborative Platform for Prompt Engineering and Dataset Curation",
    author = "Al-shaibani, Maged S.  and
      Alyafeai, Zaid  and
      Refai, Dania  and
      Alomari, Nawaf  and
      Ashraf, Ahmed  and
      Alheraki, Mais  and
      Alturki, Mustafa  and
      Luqman, Hamzah  and
      Ahmad, Irfan",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-demo.18/",
    pages = "225--260",
    ISBN = "979-8-89176-382-1",
    abstract = "PromptLab is a web-based platform for collaborative prompt engineering across diverse natural language processing tasks and datasets. The platform addresses primary challenges in prompt development, including template creation, collaborative review, and quality assurance through a comprehensive workflow that supports both individual researchers and team-based projects. PromptLab integrates with HuggingFace and provides AI-assisted prompt generation via OpenRouter[{\ensuremath{<}}https://openrouter.ai/{\ensuremath{>}}], and supporting real-time validation with multiple Large Language Models (LLMs). The platform features a flexible templating system using Jinja2, role-based project management, peer review processes, and supports programmatic access through RESTful APIs. To ensure data privacy and support sensitive research environments, PromptLab includes an easy CI/CD pipeline for self-hosted deployments and institutional control. We demonstrate the platform{'}s effectiveness through two evaluations: a controlled comparison study with six researchers across five benchmark datasets and 13 models with 90 prompts; and a comprehensive case study in instruction tuning research, where over 350 prompts across 80+ datasets have been developed and validated by multiple team members. The platform is available at \url{https://promptlab.up.railway.app} and the source code is available on GitHub at \url{https://github.com/KFUPM-JRCAI/PromptLab}."
}