Large language models (LLMs) are impressive in their ability to hold human-like conversations, making them attractive for chatbot applications across diverse fields. But their tendency to veer off-topic, especially in specialized areas like healthcare or legal advice, makes ensuring they provide accurate and relevant information in those domains challenging. How do we keep these AI conversationalists focused? Researchers are exploring ways to automatically create structured dialog flows that act like guardrails, preventing chatbots from derailing into irrelevant discussions. Imagine these flows as conversational maps, guiding the chatbot through appropriate responses in a specific domain. The research introduces two innovative approaches. One uses the LLM's existing knowledge to generate a general dialog flow based on typical conversation patterns. The second, more interesting approach analyzes real conversations from a specific domain, automatically identifying key actions and transitions between user and chatbot. This data then informs the creation of a domain-specific dialog flow. By incorporating real-world conversations, the system effectively learns how humans interact within that area, keeping the chatbot’s responses focused and relevant. This approach demonstrates how domain knowledge, automatically extracted from actual conversations, can significantly improve an LLM-based chatbot’s performance. The researchers found that by using these data-guided flows, they could achieve much better domain coverage and accuracy, essentially teaching LLMs to stay on topic without extensive manual intervention. While the work shows promise, it also has limitations. For now, research has been confined to task-oriented dialogs. Future research could explore more open-ended conversational domains, or explore other ways to make use of labeled conversation datasets to further refine how these dialog flow maps are constructed, moving one step closer to building truly helpful and reliable domain-specific AI chatbots.