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Prompt Orchestration Markup Language

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arxiv 2508.13948 v1 pith:QA37IJOR submitted 2025-08-19 cs.HC cs.AIcs.CLcs.PL

Prompt Orchestration Markup Language

Yuge Zhang , Nan Chen , Jiahang Xu , Yuqing Yang This is my paper
classification cs.HC cs.AIcs.CLcs.PL
keywords dataintegrationlanguagemarkuppomlcomplexcomprehensiveorchestration
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Language for Describing Agentic LLM Contexts

    cs.AI 2026-05 accept novelty 7.0

    ACDL is a language for specifying the structure and dynamics of LLM input contexts in agent systems using constructs for roles, dynamic content, time references, and conditional structures.

  2. A Prompt-Aware Structuring Framework for Reliable Reuse of AI-Generated Content in the Agentic Web

    cs.AI 2026-05 unverdicted novelty 5.0

    A framework structures AI-generated content with prompt-aware metadata and verifiable credentials to support reliable assessment and reuse by agents.

  3. When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning

    cs.SE 2025-09 conditional novelty 5.0

    LLM-based sketch generation for embedded ML is fragile, with success rates below 40%, and prompt structure alone can swing outcomes from 15% to 30%.