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Imprompt: A Language Framework for Prompt Programming

arXiv 2607.22683 ↗First posted 13 Jul 2026cs.AI cs.CL cs.PLcited by 0

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Abstract (arXiv)

With the unprecedented success of Language Models (LMs), the science of Prompt Engineering has evolved the powerful idea of Prompt Programming, where prompts are treated as a programmable control surface for describing complex tasks and leveraging LM capabilities. However, existing prompt programming frameworks suffer from various complexities and inelegances, which make them hard to utilize in practice for effectively describing tasks. We propose Imprompt, a new language framework for the study and practice of prompt programming. We undertake a foundational investigation of prompt programming, and contend that prompt programs must contain only the task descriptions and must be decoupled from lower-level 'execution' details. We further develop this position by illustrating structured prompting as a combination of prompt programming and prompt program 'compilation'. We exemplify this view by formally defining two compilers for Imprompt programs. We then explore the idea of typing for prompt programs and draw a correspondence between type checking and constrained decoding. Finally, we implement our compilers and type checkers and evaluate them on a variety of case studies. We believe our work contributes programming-language foundations toward the emerging area of prompt programming.

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References · 37

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25 more
  1. 2022Large Language Models are Better Reasoners with Self-Verificationcited by 304
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  18. 2025From Prompts to Templates: A Systematic Prompt Template Analysis for Real-world LLMappscited by 19
  19. 2025Solving a Million-Step LLM Task with Zero Errorscited by 18
  20. 2025DINGO: Constrained Inference for Diffusion LLMscited by 15
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  24. 2024Opportunistically Parallel Lambda Calculuscited by 5
  25. 2025RefineStat: Efficient Exploration for Probabilistic Program Synthesiscited by 5

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