Understanding uncertainties present in complex systems, such as self-adaptive robotics, their potential impacts, and mitigation strategies has always been a major challenge for researchers and industry professionals. This hands-on technical briefing will present novel approaches, a tool, and a taxonomy to systematically and automatically identify uncertainty in self-adaptive robotics using large language models (LLMs). The content of this briefing is being developed as part of an EU project named RoboSAPIENS, which aims at ensuring the trustworthiness of self-adaptive robots. To assist industry professionals in the uncertainty identification process, we developed an LLM-based uncertainty identification approach and implemented it in a tool. This briefing will cover LLM-based uncertainty identification, prompting techniques, uncertainty taxonomy, hands-on tool sessions, and experiences from our interactions with industry professionals.