Imagine effortlessly uncovering the complex logic that makes a chatbot engaging. Researchers at NVIDIA are doing just that with a groundbreaking method to automatically extract dialogue policies directly from conversation data. This innovative approach transforms raw, unstructured conversations into a clear roadmap of user-bot interactions. Think of it like reverse-engineering a chatbot's brain, revealing the paths users take, the bot's typical responses, and even how it handles unexpected turns. NVIDIA's secret weapon is a combination of large language models (LLMs) and graph-based algorithms. First, they use LLMs to translate dialogue turns into a simplified format, essentially summarizing each interaction. Next, they connect these summaries into a flow network, creating a visual map of the entire conversation. Finally, by applying path-finding algorithms to this network, they surface the dominant dialogue patterns, revealing the chatbot’s core logic. This new technique is proving superior to previous methods that relied solely on prompting LLMs. It’s not only more efficient but also offers greater control and transparency, giving developers the ability to fine-tune and adapt chatbots with ease. By converting complex conversations into interpretable graphs, this work is transforming chatbot development, paving the way for more efficient and engaging conversational experiences. Future work will focus on improving intent identification and scaling the method to various conversation datasets, further refining our ability to analyze and design compelling chatbot interactions.