mlsanityData to 14 Oct 2026

PDL: A Declarative Prompt Programming Language

arXiv 2410.19135 ↗First posted 24 Oct 2024cs.AI cs.PLcited by 14

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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.

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

15 of 32 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  6. 2021Program Synthesis with Large Language Modelscited by 3,637
  7. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  8. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  9. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
  10. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  11. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
  12. 2023ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Modelscited by 184
3 more
  1. 2024Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Taskscited by 52
  2. 2023Prompting Frameworks for Large Language Models: A Surveycited by 29
  3. 2023Formally Specifying the High-Level Behavior of LLM-Based Agentscited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 9.

14 papers.

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  1. 2025AutoPDL: Automatic Prompt Optimization for LLM Agentscited by 27
  2. 2025Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocolcited by 6
  3. 2026AgentSPEX: An Agent SPecification and EXecution Languagecited by 5
  4. 2026Neuro-Symbolic Verification on Instruction Following of LLMscited by 4
  5. 2025One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligencecited by 2
  6. 2025Sharing State Between Prompts and Programscited by 2
  7. 2026Resolving Java Code Repository Issues with iSWE Agentcited by 1
  8. 2026A Language for Describing Agentic LLM Contextscited by 1
  9. 2026Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectorycited by 1
  10. 2026What makes prompts a graph: necessary and sufficient conditions for prompt graph engineeringcited by 1
  11. 2025Representing Prompting Patterns with PDL: Compliance Agent Case Studycited by 0
  12. 2026Imprompt: A Language Framework for Prompt Programmingcited by 0
2 more
  1. 2026PPDL: LLM-Based Flows as Probabilistic Programscited by 0
  2. 2026Agents as Software: A Programming Languages Agenda for Agent Reliabilitycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts
  2. 2024LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
  3. 2026Structured Prompt Language: Declarative Context Management for LLMs
  4. 2026The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure
  5. 2025PRL: Prompts from Reinforcement Learning
  6. 2025Pel, A Programming Language for Orchestrating AI Agents
  7. 2024Prompt Design and Engineering: Introduction and Advanced Methods
  8. 2026Optimizing LLM Prompt Engineering with DSPy Based Declarative Learning