Today, creating chatbots that handle complex tasks naturally yet remain under our control is a significant challenge. Traditional graph-based chatbots offer control but can feel rigid and unnatural, while modern LLM (Large Language Model) chatbots excel in naturalness but can veer off course in complex interactions. This article introduces a hybrid approach, combining the best of both worlds: the precision of graph-based systems with the fluid conversation style of LLMs. Our goal is to provide a solution that makes chatbots both more engaging for users and easier to manage for developers, especially in intricate tasks. We'll explore how this innovative method can transform chatbot interactions, making them feel more natural without sacrificing control.
In the pre-LLM days conversations were built using intent detection and directed graphs. The way it starts is a conversation designer builds a graph structure where all of the nodes are the things you