  The widespread availability of Large Language Models (LLMs) within Integrated
Development Environments (IDEs) has led to their speedy adoption.
Conversational interactions with LLMs enable programmers to obtain natural
language explanations for various software development tasks. However, LLMs
often leap to action without sufficient context, giving rise to implicit
assumptions and inaccurate responses. Conversations between developers and LLMs
are primarily structured as question-answer pairs, where the developer is
responsible for asking the the right questions and sustaining conversations
across multiple turns. In this paper, we draw inspiration from interaction
patterns and conversation analysis -- to design Robin, an enhanced
conversational AI-assistant for debugging. Through a within-subjects user study
with 12 industry professionals, we find that equipping the LLM to -- (1)
leverage the insert expansion interaction pattern, (2) facilitate turn-taking,
and (3) utilize debugging workflows -- leads to lowered conversation barriers,
effective fault localization, and 5x improvement in bug resolution rates.
