  When interacting with Retrieval-Augmented Generation (RAG)-based
conversational agents, the users must carefully craft their queries to be
understood correctly. Yet, understanding the system's capabilities can be
challenging for the users, leading to ambiguous questions that necessitate
further clarification. This work aims to bridge the gap by developing a
suggestion question generator. To generate suggestion questions, our approach
involves utilizing dynamic context, which includes both dynamic few-shot
examples and dynamically retrieved contexts. Through experiments, we show that
the dynamic contexts approach can generate better suggestion questions as
compared to other prompting approaches.
