This chapter presents clustering and topic modeling as important techniques in natural language processing (NLP) for qualitative research. It highlights topic modeling as a specialized form of clustering aimed at uncovering hidden thematic structures within text datasets. Large language models (LLMs) improve the interpretability and coherence of topics compared with traditional methods. This chapter also addresses limitations such as topic instability and model hallucinations. Practical code examples illustrate the implementation of topic generation and assignment, thereby fostering a deeper understanding of the applicability of this NLP task in qualitative analysis.