Promptware Engineering: Software Engineering for Prompt-Enabled Systems
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Abstract (arXiv)
Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2025.
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Citing papers per year: 2025: 1; 2026: 5.
6 papers.
- 2026Natural-Language Agent Harnessescited by 54
- 2026Codified Context: Infrastructure for AI Agents in a Complex Codebasecited by 5
- 2025A Survey of LLM Prompt Datasets: Taxonomy, Linguistic Patterns, and Practical Usescited by 2
- 2026Architecture Without Architects: How AI Coding Agents Shape Software Architecturecited by 2
- 2026Iterative Audit Convergence in LLM-Managed Multi-Agent Systems: A Case Study in Prompt-Engineering Quality Assurancecited by 0
- 2026Natural-Language Workflows Are Not Software Yet: Artifact-Driven Compilation for Reliable Agent Executioncited by 0
- 2026Natural-Language Workflows Are Not Software Yet: Artifact-Driven Compilation for Reliable Agent Execution21 Aug 2026
- 2026Iterative Audit Convergence in LLM-Managed Multi-Agent Systems: A Case Study in Prompt-Engineering Quality Assurance12 May 2026
- 2026Architecture Without Architects: How AI Coding Agents Shape Software Architecture5 Apr 2026
- 2026Natural-Language Agent Harnesses26 Mar 2026
- 2026Codified Context: Infrastructure for AI Agents in a Complex Codebase24 Feb 2026
- 2025A Survey of LLM Prompt Datasets: Taxonomy, Linguistic Patterns, and Practical Uses10 Oct 2025