In the rapidly growing landscape of ChatGPT-like applications, Retrieval-Augmented Generation (RAG) solutions are gaining significant traction. However, analyzing the user-generated data from these applications to extract meaningful insights and adapt to emerging trends is a complex challenge.

This workshop introduces Bunka, an open-source library designed for intuitive exploration and visualization of unstructured datasets. Using Bunka, participants will explore embeddings in simplified projections, empowering them to uncover hidden patterns and track semantic drift effectively.

We will work with a unique, anonymized dataset of recently leaked chat channels from Andrew Tate’s private community, tackling real-world challenges to extract actionable insights. The session will focus on techniques to:
• Analyze user behavior and emerging trends in chat data.
• Monitor semantic drift and its impact on application performance.
• Adaptively predict and respond to new themes and trends.

By the end of this hands-on workshop, you’ll gain practical experience with embedding-based analysis and actionable insights into leveraging semantic monitoring to enhance your LLM-driven applications.