Text Segmentation involves dividing text into coherent sections, typically defined by topics. Over the past decade, lots of research has gone into furthering the development of supervised techniques to approach TS tasks, which has largely left unsupervised TS techniques with less advancement. With the onset of Large Language Models and the accessibility of them becoming more commonplace, unsupervised TS can benefit. By leveraging an LLM’s strong understanding of natural language, prompting appropriately, and feeding in valuable context, we show that, even with locally run, open-source LLM models, we can achieve state-of-the-art unsupervised TS results as benchmarked by \(P_k\) and WindowDiff scores.