Prompt Orchestration Markup Language
Abstract
Large Language Models (LLMs) require sophisticated prompting, yet current practices face challenges in structure, data integration, format sensitivity, and tooling. Existing methods lack comprehensive solutions for organizing complex prompts involving diverse data types (documents, tables, images) or managing presentation variations systematically. To address these gaps, we introduce POML (Prompt Orchestration Markup Language). POML employs component-based markup for logical structure (roles, tasks, examples), specialized tags for seamless data integration, and a CSS-like styling system to decouple content from presentation, reducing formatting sensitivity. It includes templating for dynamic prompts and a comprehensive developer toolkit (IDE support, SDKs) to improve version control and collaboration. We validate POML through two case studies demonstrating its impact on complex application integration (PomLink) and accuracy performance (TableQA), as well as a user study assessing its effectiveness in real-world development scenarios.
Cite
@article{arxiv.2508.13948,
title = {Prompt Orchestration Markup Language},
author = {Yuge Zhang and Nan Chen and Jiahang Xu and Yuqing Yang},
journal= {arXiv preprint arXiv:2508.13948},
year = {2025}
}
Comments
All findings in this paper are derived from a POML snapshot as of February 2025