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PDL: A Declarative Prompt Programming Language
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Large language models (LLMs) have taken the world by storm by making many previously difficult uses of AI feasible. LLMs are controlled via highly expressive textual prompts and return textual answers. Unfortunately, this unstructured text as input and output makes LLM-based applications brittle. This motivates the rise of prompting frameworks, which mediate between LLMs and the external world. However, existing prompting frameworks either have a high learning curve or take away control over the exact prompts from the developer. To overcome this dilemma, this paper introduces the Prompt Declaration Language (PDL). PDL is a simple declarative data-oriented language that puts prompts at the forefront, based on YAML. PDL works well with many LLM platforms and LLMs. It supports writing interactive applications that call LLMs and tools, and makes it easy to implement common use-cases such as chatbots, RAG, or agents. We hope PDL will make prompt programming simpler, less brittle, and more enjoyable.
Forward citations
Cited by 4 Pith papers
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Imprompt: A Language Framework for Prompt Programming
Prompt programs can be typed like programs and compiled in different ways; Imprompt formalizes two such compilers and tests them on tax-law reasoning and data-labeling tasks.
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What makes prompts a graph: necessary and sufficient conditions for prompt graph engineering
The paper defines prompt graph engineering via four necessary and sufficient conditions (explicit structure, structure/content separation, executable semantics, first-class artifact) and an inclusion/exclusion test th...
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Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol
This position paper classifies testing methods for LLM applications into three layers and proposes AICL, a structured protocol for testable agent communication; neither the framework nor the protocol is empirically validated.
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Representing Prompting Patterns with PDL: Compliance Agent Case Study
A PDL-based compliance agent outperformed a canned ReAct agent by up to 4x, mainly by cutting tool call failures.
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