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

TL;DR
POML is a new markup language designed to improve the organization, data integration, and presentation of prompts for large language models, enhancing development and application complexity management.
Contribution
It introduces a component-based markup system with styling, templating, and developer tools to systematically address prompt structuring challenges.
Findings
Enhanced prompt organization in case studies
Improved accuracy in TableQA task
Positive user feedback on development efficiency
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…
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