  This paper delves into an advanced implementation of
Chain-of-Thought-Prompting in Large Language Models, focusing on the use of
tools (or "plug-ins") within the explicit reasoning paths generated by this
prompting method. We find that tool-enabled conversational agents often become
sidetracked, as additional context from tools like search engines or
calculators diverts from original user intents. To address this, we explore a
concept wherein the user becomes the tool, providing necessary details and
refining their requests. Through Conversation Analysis, we characterize this
interaction as insert-expansion - an intermediary conversation designed to
facilitate the preferred response. We explore possibilities arising from this
'user-as-a-tool' approach in two empirical studies using direct comparison, and
find benefits in the recommendation domain.
