  Digital assistants have become ubiquitous in e-commerce applications,
following the recent advancements in Information Retrieval (IR), Natural
Language Processing (NLP) and Generative Artificial Intelligence (AI). However,
customers are often unsure or unaware of how to effectively converse with these
assistants to meet their shopping needs. In this work, we emphasize the
importance of providing customers a fast, easy to use, and natural way to
interact with conversational shopping assistants. We propose a framework that
employs Large Language Models (LLMs) to automatically generate contextual,
useful, answerable, fluent and diverse questions about products, via in-context
learning and supervised fine-tuning. Recommending these questions to customers
as helpful suggestions or hints to both start and continue a conversation can
result in a smoother and faster shopping experience with reduced conversation
overhead and friction. We perform extensive offline evaluations, and discuss in
detail about potential customer impact, and the type, length and latency of our
generated product questions if incorporated into a real-world shopping
assistant.
