  LLM-driven dialog systems are used in a diverse set of applications, ranging
from healthcare to customer service. However, given their generalization
capability, it is difficult to ensure that these chatbots stay within the
boundaries of the specialized domains, potentially resulting in inaccurate
information and irrelevant responses. This paper introduces an unsupervised
approach for automatically inducing domain-specific dialog flows that can be
used to constrain LLM-based chatbots. We introduce two variants of dialog flow
based on the availability of in-domain conversation instances. Through human
and automatic evaluation over various dialog domains, we demonstrate that our
high-quality data-guided dialog flows achieve better domain coverage, thereby
overcoming the need for extensive manual crafting of such flows.
