Bridging UI Design and chatbot Interactions: Applying Form-Based Principles to Conversational Agents

TL;DR

This paper introduces a method to incorporate GUI-inspired metaphors into chatbot interactions using structured prompts, enhancing clarity and coherence in multi-turn domain-specific conversations.

Contribution

It proposes modeling acknowledgment and reset actions as explicit tasks in LLM prompts to improve context management in chatbots.

Findings

Improved multi-turn task coherence in chatbot interactions

Enhanced user satisfaction and efficiency

Effective handling of context switching and acknowledgment

Abstract

Domain specific chatbot applications often involve multi step interactions, such as refining search filters, selecting multiple items, or performing comparisons. Traditional graphical user interfaces (GUIs) handle these workflows by providing explicit "Submit" (commit data) and "Reset" (discard data) actions, allowing back-end systems to track user intent unambiguously. In contrast, conversational agents rely on subtle language cues, which can lead to confusion and incomplete context management. This paper proposes modeling these GUI inspired metaphors acknowledgment (submit like) and context switching (reset-like) as explicit tasks within large language model (LLM) prompts. By capturing user acknowledgment, reset actions, and chain of thought (CoT) reasoning as structured session data, we preserve clarity, reduce user confusion, and align domain-specific chatbot interactions with back-end logic. We demonstrate our approach in hotel booking and customer management scenarios, highlighting improvements in multi-turn task coherence, user satisfaction, and efficiency.