Representing Prompting Patterns with PDL: Compliance Agent Case Study
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909681050451968 |
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| author | Vaziri, Mandana Mandel, Louis Watanabe, Yuji Kitahara, Hirokuni Hirzel, Martin Sailer, Anca |
| author_facet | Vaziri, Mandana Mandel, Louis Watanabe, Yuji Kitahara, Hirokuni Hirzel, Martin Sailer, Anca |
| contents | Prompt engineering for LLMs remains complex, with existing frameworks either hiding complexity behind restrictive APIs or providing inflexible canned patterns that resist customization -- making sophisticated agentic programming challenging. We present the Prompt Declaration Language (PDL), a novel approach to prompt representation that tackles this fundamental complexity by bringing prompts to the forefront, enabling manual and automatic prompt tuning while capturing the composition of LLM calls together with rule-based code and external tools. By abstracting away the plumbing for such compositions, PDL aims at improving programmer productivity while providing a declarative representation that is amenable to optimization. This paper demonstrates PDL's utility through a real-world case study of a compliance agent. Tuning the prompting pattern of this agent yielded up to 4x performance improvement compared to using a canned agent and prompt pattern. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Representing Prompting Patterns with PDL: Compliance Agent Case Study Vaziri, Mandana Mandel, Louis Watanabe, Yuji Kitahara, Hirokuni Hirzel, Martin Sailer, Anca Artificial Intelligence Machine Learning Programming Languages Software Engineering Prompt engineering for LLMs remains complex, with existing frameworks either hiding complexity behind restrictive APIs or providing inflexible canned patterns that resist customization -- making sophisticated agentic programming challenging. We present the Prompt Declaration Language (PDL), a novel approach to prompt representation that tackles this fundamental complexity by bringing prompts to the forefront, enabling manual and automatic prompt tuning while capturing the composition of LLM calls together with rule-based code and external tools. By abstracting away the plumbing for such compositions, PDL aims at improving programmer productivity while providing a declarative representation that is amenable to optimization. This paper demonstrates PDL's utility through a real-world case study of a compliance agent. Tuning the prompting pattern of this agent yielded up to 4x performance improvement compared to using a canned agent and prompt pattern. |
| title | Representing Prompting Patterns with PDL: Compliance Agent Case Study |
| topic | Artificial Intelligence Machine Learning Programming Languages Software Engineering |
| url | https://arxiv.org/abs/2507.06396 |