Imagine having a conversation with a recommendation system that truly understands your needs, even when you express them indirectly. Instead of relying on rigid, pre-defined categories, this system dives into the rich world of user reviews to find hidden gems perfectly tailored to your unique preferences. That's the promise of Retrieval-Augmented Conversational Recommendation (RA-Rec), a cutting-edge approach that's transforming how we discover products, services, and experiences. Traditional recommendation systems often struggle with nuanced language. Saying "I'm watching my weight" doesn't neatly translate into a specific restaurant category. RA-Rec tackles this challenge by leveraging the power of Large Language Models (LLMs) to analyze user reviews, unlocking a treasure trove of nuanced information. By understanding the complex language within reviews, RA-Rec can connect your indirect preferences to items that truly match. For example, it can link your desire for healthy options to a restaurant review mentioning "lots of low-cal veggie options." This approach goes beyond simple keyword matching, delving into the meaning and sentiment expressed in reviews. RA-Rec also employs a clever semi-structured approach to dialogue state tracking. It uses predefined categories like location and cuisine type, but allows the LLM to generate natural language descriptions within those categories. This flexibility captures the nuances of your preferences while maintaining a structured approach to the conversation. The system also excels at question answering. If you ask about parking, RA-Rec can intelligently determine whether to look for the answer in structured metadata or dive into the more nuanced world of user reviews. This dynamic approach ensures you get the most accurate and relevant information. RA-Rec represents a significant leap forward in conversational recommendation systems. By combining the power of LLMs with the richness of user reviews, it creates a more intuitive, personalized, and ultimately, more satisfying recommendation experience. While the current demonstration focuses on restaurant recommendations, the underlying technology is adaptable to various domains, from product recommendations to travel planning. Future research directions include actively eliciting user preferences, enabling more complex reasoning about those preferences, and even negotiating trade-offs between different recommendations. The future of recommendations is conversational, and RA-Rec is leading the way.