Prompt Orchestration Markup Language
Authors: name: Yuge Zhang, name: Nan Chen, name: Jiahang Xu, name: Yuqing Yang

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.