"Make it Pop, but not Like That": A Taxonomy of Iterative Prompting Strategies for Refining AI-Generated Web Interfaces

Abstract The rapid proliferation of Large Language Models (LLMs) and generative tools (e.g., GPT-4, Tongyi Lingma, Trae) has fundamentally democratized the landscape of web development, shifting the paradigm from manual syntax construction to natural language intent specification. However, while the barrier to "drafting" initial code has been lowered, a significant "Refinement Crisis" has emerged. As task complexity scales from static landing pages to dynamic, stateful applications, novice users encounter a profound "Gulf of Evaluation" when attempting to repair AI-generated errors. Unlike text generation, where errors are semantic and visible, web interface generation involves a complex interplay between visual presentation (CSS) and invisible state management (JavaScript). In this paper, we present a large-scale observational study with 200 novice participants tasked with utilizing IDE-integrated AI assistants to build a fully functional CRUD (Create, Read, Update, Delete) note-taking application. Through a rigorous analysis of interaction logs and source code snapshots, we reveal that while 90% of users could generate a baseline prototype, 80% encountered severe "invisible state" breakdowns (e.g., data persistence failure), and 50% suffered from persistent layout regressions . We contribute a detailed taxonomy of four repair strategies: Perceptual Refinement , Behavioral Correction , Diagnostic Proxy , and Global Reset . Furthermore, we characterize the "Whack-a-Mole" effect —a phenomenon where repairing visual elements inadvertently corrupts functional logic due to the AI's lack of holistic state awareness. Our findings provide empirical evidence for the limitations of current chat-based coding interfaces and offer critical design implications for future "State-Aware" AI IDEs that reify invisible data flows to bridge the gap between user intent and execution.