🤖 AI Summary
This study investigates whether large language models (LLMs) can efficiently construct and maintain large-scale, multi-module software systems using only natural language prompts, particularly in emerging programming language ecosystems lacking mature toolchains. Leveraging Claude Code (Opus 4.5), the authors employed a fully prompt-driven workflow to develop a 7,420-line terminal user interface (TUI) framework within three days—implementing production-grade features such as window management, event-driven architecture, and interactive components—without writing any code manually. The project was realized through 107 concise prompts, enabling highly iterative development. This work provides the first empirical evidence that LLMs can sustain architectural consistency throughout the construction of complex software systems, thereby validating prompt-driven development as a viable new paradigm in software engineering.
📝 Abstract
Large language models are increasingly used in software development, yet their ability to generate and maintain large, multi module systems through natural language interaction remains insufficiently characterized. This study presents an empirical analysis of developing a 7420 line Terminal User Interface framework for the Ring programming language, completed in roughly ten hours of active work spread across three days using a purely prompt driven workflow with Claude Code, Opus 4.5. The system was produced through 107 prompts: 21 feature requests, 72 bug fix prompts, 9 prompts sharing information from Ring documentation, 4 prompts providing architectural guidance, and 1 prompt dedicated to generating documentation. Development progressed across five phases, with the Window Manager phase requiring the most interaction, followed by complex UI systems and controls expansion. Bug related prompts covered redraw issues, event handling faults, runtime errors, and layout inconsistencies, while feature requests focused primarily on new widgets, window manager capabilities, and advanced UI components. Most prompts were short, reflecting a highly iterative workflow in which the human role was limited to specifying requirements, validating behaviour, and issuing corrective prompts without writing any code manually. The resulting framework includes a complete windowing subsystem, event driven architecture, interactive widgets, hierarchical menus, grid and tree components, tab controls, and a multi window desktop environment. By combining quantitative prompt analysis with qualitative assessment of model behaviour, this study provides empirical evidence that modern LLMs can sustain architectural coherence and support the construction of production grade tooling for emerging programming languages, highlighting prompt driven development as a viable methodology within software engineering practice.