PDL: A Declarative Prompt Programming Language

TL;DR

This paper introduces PDL, a declarative, data-oriented language based on YAML, designed to simplify prompt programming for large language models, making it more robust and accessible.

Contribution

The paper presents PDL, a novel declarative language that improves prompt control and usability across various LLM platforms and applications.

Findings

PDL supports multiple LLM platforms and models.

It simplifies prompt management and application development.

PDL enhances robustness and reduces brittleness in LLM prompts.

Abstract

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.