Qualitative bottom-up coding is essential for identifying themes and patterns in complex data. This tutorial demonstrates how LLMs such as ChatGPT can support the qualitative coding process for software engineering research. Participants will walk through an entire coding exercise, learning to identify themes through open coding, consolidate themes by refining and merging codes, and conduct inter-rater agreement by standardizing codebooks and testing agreement between human and AI coders. Using hands-on exercises and real-world examples, this session highlights effective human-AI collaboration and strategies to ensure transparency, trustworthiness, and methodological rigor. Designed for researchers at all levels of experience, this tutorial equips participants with practical techniques to analyze software engineering data.