# CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming

arXiv: https://arxiv.org/abs/2310.09235 · HTML: https://mlsanity.com/paper/2310.09235/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2023.

## Built on by (most cited first, 16 of 16)

- [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024)
- [How Far Are We From AGI: Are LLMs All We Need?](https://mlsanity.com/paper/2405.10313/) (2024)
- [What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use](https://mlsanity.com/paper/2409.08775/) (2024)
- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration](https://mlsanity.com/paper/2604.17148/) (2026)
- [MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation](https://mlsanity.com/paper/2407.01972/) (2024)
- [Wordflow: Social Prompt Engineering for Large Language Models](https://mlsanity.com/paper/2401.14447/) (2024)
- [Human-AI Experience in Integrated Development Environments: A Systematic Literature Review](https://mlsanity.com/paper/2503.06195/) (2025)
- [Sharing State Between Prompts and Programs](https://mlsanity.com/paper/2512.14805/) (2025)
- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)
- [Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming](https://mlsanity.com/paper/2604.27134/) (2026)
- [Understanding Prompt Programming Tasks and Questions](https://mlsanity.com/paper/2507.17264/) (2025)
- [Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations](https://mlsanity.com/paper/2509.25492/) (2025)
- [A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI](https://mlsanity.com/paper/2510.26275/) (2025)
- [Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learning](https://mlsanity.com/paper/2601.12134/) (2026)
- [Componentization: Decomposing Monolithic LLM Responses into Manipulable Semantic Units](https://mlsanity.com/paper/2509.08203/) (2025)

## Abstract (arXiv)

Natural language (NL) programming has become more approachable due to the powerful code-generation capability of large language models (LLMs). This shift to using NL to program enhances collaborative programming by reducing communication barriers and context-switching among programmers from varying backgrounds. However, programmers may face challenges during prompt engineering in a collaborative setting as they need to actively keep aware of their collaborators' progress and intents. In this paper, we aim to investigate ways to assist programmers' prompt engineering in a collaborative context. We first conducted a formative study to understand the workflows and challenges of programmers when using NL for collaborative programming. Based on our findings, we implemented a prototype, CoPrompt, to support collaborative prompt engineering by providing referring, requesting, sharing, and linking mechanisms. Our user study indicates that CoPrompt assists programmers in comprehending collaborators' prompts and building on their collaborators' work, reducing repetitive updates and communication costs.