# PDL: A Declarative Prompt Programming Language

arXiv: https://arxiv.org/abs/2410.19135 · HTML: https://mlsanity.com/paper/2410.19135/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models](https://mlsanity.com/paper/2305.18323/) (2023)
- [Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks](https://mlsanity.com/paper/2407.00121/) (2024)
- [Prompting Frameworks for Large Language Models: A Survey](https://mlsanity.com/paper/2311.12785/) (2023)
- [Formally Specifying the High-Level Behavior of LLM-Based Agents](https://mlsanity.com/paper/2310.08535/) (2023)

## Built on by (most cited first, 14 of 14)

- [AutoPDL: Automatic Prompt Optimization for LLM Agents](https://mlsanity.com/paper/2504.04365/) (2025)
- [Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol](https://mlsanity.com/paper/2508.20737/) (2025)
- [AgentSPEX: An Agent SPecification and EXecution Language](https://mlsanity.com/paper/2604.13346/) (2026)
- [Neuro-Symbolic Verification on Instruction Following of LLMs](https://mlsanity.com/paper/2601.17789/) (2026)
- [One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence](https://mlsanity.com/paper/2506.10157/) (2025)
- [Sharing State Between Prompts and Programs](https://mlsanity.com/paper/2512.14805/) (2025)
- [Resolving Java Code Repository Issues with iSWE Agent](https://mlsanity.com/paper/2603.11356/) (2026)
- [A Language for Describing Agentic LLM Contexts](https://mlsanity.com/paper/2605.01920/) (2026)
- [Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory](https://mlsanity.com/paper/2606.06523/) (2026)
- [What makes prompts a graph: necessary and sufficient conditions for prompt graph engineering](https://mlsanity.com/paper/2607.27578/) (2026)
- [Representing Prompting Patterns with PDL: Compliance Agent Case Study](https://mlsanity.com/paper/2507.06396/) (2025)
- [Imprompt: A Language Framework for Prompt Programming](https://mlsanity.com/paper/2607.22683/) (2026)
- [PPDL: LLM-Based Flows as Probabilistic Programs](https://mlsanity.com/paper/2608.05234/) (2026)
- [Agents as Software: A Programming Languages Agenda for Agent Reliability](https://mlsanity.com/paper/2609.32198/) (2026)

## Abstract (arXiv)

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.