Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering
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Abstract (arXiv)
Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 21 Sep 2025.
Builds on
17 of 37 references.
References · 17
- 2024POSIX: A Prompt Sensitivity Index For Large Language Modelscited by 59
- 2024Human-In-the-Loop Software Development Agentscited by 42
- 2024Language Models for Code Completion: A Practical Evaluationcited by 39
- 2024Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplarscited by 37
- 2024PromptWizard: Task-Aware Prompt Optimization Frameworkcited by 36
- 2024Prompt Optimization with Human Feedbackcited by 35
- 2023Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomiescited by 34
- 2025From Prompts to Templates: A Systematic Prompt Template Analysis for Real-world LLMappscited by 21
- 2024Prompts Are Programs Too! Understanding How Developers Build Software Containing Promptscited by 19
- 2024Prompting in the Wild: An Empirical Study of Prompt Evolution in Software Repositoriescited by 18
- 2025Prompting in the Dark: Assessing Human Performance in Prompt Engineering for Data Labeling When Gold Labels Are Absentcited by 15
- 2025Promptomatix: An Automatic Prompt Optimization Framework for Large Language Modelscited by 12
5 more
- 2024Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Testscited by 11
- 2023Revisiting Automated Prompting: Are We Actually Doing Better?cited by 7
- 2025Collective Reasoning Among LLMs: A Framework for Answer Validation Without Ground Truthcited by 7
- 2025HPSS: Heuristic Prompting Strategy Search for LLM Evaluatorscited by 5
- 2025A Multi-agent Onboarding Assistant based on Large Language Models, Retrieval Augmented Generation, and Chain-of-Thoughtcited by 2
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