  Large language model (LLM) applications, such as ChatGPT, are a powerful tool
for online information-seeking (IS) and problem-solving tasks. However, users
still face challenges initializing and refining prompts, and their cognitive
barriers and biased perceptions further impede task completion. These issues
reflect broader challenges identified within the fields of IS and interactive
information retrieval (IIR). To address these, our approach integrates task
context and user perceptions into human-ChatGPT interactions through prompt
engineering. We developed a ChatGPT-like platform integrated with supportive
functions, including perception articulation, prompt suggestion, and
conversation explanation. Our findings of a user study demonstrate that the
supportive functions help users manage expectations, reduce cognitive loads,
better refine prompts, and increase user engagement. This research enhances our
comprehension of designing proactive and user-centric systems with LLMs. It
offers insights into evaluating human-LLM interactions and emphasizes potential
challenges for under served users.
