Large Language Models (LLMs), such as ChatGPT, exhibit advanced capabilities in generating text, images, and videos. However, their effective use remains constrained by challenges in prompt formulation, personalization, and opaque decision-making processes. To investigate these challenges and identify design opportunities, we conducted a two-phase qualitative study. In Phase 1, we performed in-depth interviews with eight everyday LLM users after they engaged in structured tasks using ChatGPT across both familiar and unfamiliar domains. Our findings revealed key user difficulties in constructing effective prompts, iteratively refining AI-generated responses, and assessing response reliability especially in domains beyond users' expertise. Informed by these insights, we designed a high-fidelity prototype incorporating Reflective Prompting, Section Regeneration, Input-Output Mapping, Confidence Indicators, and a Customization Panel. In Phase 2, user testing of the prototype indicated that these interface-level improvements may prove useful for reducing cognitive load, increasing transparency, and fostering more intuitive and collaborative human-AI interactions. Our study contributes to the growing discourse on human-centred AI, advocating for human-LLM interactions that enhance user agency, transparency, and co-creative interaction, ultimately supporting more intuitive, accessible, and trustworthy generative AI systems.

翻译：以ChatGPT为代表的大型语言模型（LLMs）在生成文本、图像和视频方面展现出先进能力。然而，其有效使用仍受限于提示构建、个性化及决策过程不透明等挑战。为探究这些挑战并识别设计机遇，我们开展了一项包含两个阶段的质性研究。在第一阶段，我们对八位日常LLM用户进行了深度访谈，访谈对象均在熟悉与陌生领域中使用ChatGPT完成结构化任务。研究发现，用户在构建有效提示、迭代优化AI生成响应以及评估响应可靠性（尤其在用户专业领域之外）方面存在显著困难。基于这些发现，我们设计了一个高保真原型，整合了反思性提示、分段再生、输入-输出映射、置信度指示器及自定义面板等功能。在第二阶段的原型用户测试中，结果表明这些界面层级的改进有助于降低认知负荷、提升透明度，并促进更直观协作的人机交互。本研究为日益增长的人本人工智能讨论提供了新见解，倡导通过增强用户能动性、透明度和协同创造交互来优化人类-LLM互动，最终构建更直观、易用且可信的生成式人工智能系统。