Large language models (LLMs) have generated excitement in many areas and may also make human-like conversations with social robots possible. Drawing from human-robot interaction literature and interviews, we developed Saleshat based on the commercial social robot Furhat and the large language model GPT-4. Saleshat emphasizes refined natural language processing and dynamic control of the robot’s physical appearance through the LLM. Responses from the LLM are processed sequentially, enabling the robot to react quickly. The results of our first formative evaluation with six users engaging in a sales conversation about Bluetooth speakers show that Saleshat can provide accurate and detailed responses, maintain a good conversation flow, and show dynamically controlled non-verbal cues. With our findings, we contribute to research on social robots and LLMs by providing design knowledge for LLM-based social robots and by uncovering the benefits and challenges of integrating LLMs into a social robot