OOPrompt: Reifying Intents into Structured Artifacts for Modular and Iterative Prompting

2026-04-21

AI Summary

Current linear textual prompts struggle to effectively convey users’ multidimensional intentions, leading to significant challenges in prompt construction and management during complex interactions. This work proposes Object-Oriented Prompting (OOPrompt), a novel paradigm that, for the first time, articulates a structured design space wherein natural language prompts are transformed into modular, editable, iterable, and reusable objects. By integrating human-computer interaction methodologies, prototype system development, and a two-phase user study (N=20), the research demonstrates OOPrompt’s substantial advantages in expressiveness, flexibility, and usability. The findings offer both a theoretical framework and practical guidance for designing interactive systems powered by large language models.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The rise of large language models (LLMs) has given rise to a class of prompt-based interactive systems where users primarily express their input in natural language. However, composing a prompt as a linear text string becomes unwieldy when capturing users' multifaceted intents. We present Object-Oriented Prompting (OOPrompt), an emergent interaction paradigm that enables users to create, edit, iterate, and reuse prompts as structured, manipulable artifacts, unifying and generalizing several existing point systems. We first outlined a design space from existing work and built an early prototype, which we deployed as a probe in a formative study with 20 participants. Their feedback informed an expanded OOPrompt design space. We then developed the full OOPrompt prototype and conducted a validation study to further understand OOPrompt's added values and trade-offs. We expect the OOPrompt design space to provide theoretical and empirical guidance to the design and engineering of prompt-based, LLM-enabled interactive systems.

Problem

• prompting
• large language models
• user intent
• interactive systems
• structured artifacts

Innovation

• Object-Oriented Prompting
• structured prompts
• prompt engineering
• large language models
• interactive systems