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Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts

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arxiv 2409.12447 v2 pith:MZOQN4DQ submitted 2024-09-19 cs.SE cs.AIcs.HC

classification cs.SEcs.AIcs.HC
keywords promptprogrammingmodelspromptssoftwaredevelopmentmentaldevelop
verification ladder T0 review T1 audit T2 compute T3 formal

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Generative pre-trained models power intelligent software features used by millions of users controlled by developer-written natural language prompts. Despite the impact of prompt-powered software, little is known about its development process and its relationship to programming. In this work, we argue that some prompts are programs and that the development of prompts is a distinct phenomenon in programming known as "prompt programming". We develop an understanding of prompt programming using Straussian grounded theory through interviews with 20 developers engaged in prompt development across a variety of contexts, models, domains, and prompt structures. We contribute 15 observations to form a preliminary understanding of current prompt programming practices. For example, rather than building mental models of code, prompt programmers develop mental models of the foundation model (FM)'s behavior on the prompt by interacting with the FM. While prior research shows that experts have well-formed mental models, we find that prompt programmers who have developed dozens of prompts still struggle to develop reliable mental models. Our observations show that prompt programming differs from traditional software development, motivating the creation of prompt programming tools and providing implications for software engineering stakeholders.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Prompting in the Wild: An Empirical Study of Prompt Evolution in Software Repositories

    cs.SE 2024-12 conditional novelty 7.0 of 10

    An empirical study of 1,262 prompt changes across 243 GitHub repositories shows that developers mainly add and modify prompt components during feature development, rarely document the changes, and sometimes introduce ...

  2. Type-Driven Prompt Programming: From Typed Interfaces to a Calculus of Constraints

    cs.PL 2025-08 conditional novelty 6.0 of 10

    The paper proposes a not-yet-complete dependently typed calculus for prompt programming with probabilistic refinements, and identifies gaps in constraint expressiveness and optimization algorithms.

  3. Themes of Building LLM-based Applications for Production: A Practitioner's View

    cs.SE 2024-11 conditional novelty 6.0 of 10

    Analyzing 189 practitioner YouTube videos yields 20 topics in 8 themes for building LLM applications in production, with RAG systems the most prevalent.

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