A Comparative Study of Intent-Based and Retrieval-Augmented Generation Chatbots

This paper presents a comparative study of intent-based and retrieval-augmented generation (RAG)-based chatbots, two widely used approaches in modern conversational AI systems. Chatbots are increasingly deployed across domains such as customer support, education, and enterprise services to automate query resolution and improve user experience. Intent-based chat-bots rely on predefined intents and structured responses, making them efficient for handling repetitive and well-defined queries. In contrast, RAG-based chatbots retrieve relevant information from external knowledge sources and generate context-aware responses, enabling them to handle dynamic and unseen queries more effectively. This study evaluates both approaches using a common dataset of queries categorized as known, rephrased, and unseen. The comparison is based on response accuracy, response time, flexibility, maintenance effort, and user satisfaction. An educational query dataset is used as a case study to simulate a real-world deployment scenario. The results show that intent-based chatbots perform well for structured and repetitive queries due to their speed and simplicity. However, their performance declines for rephrased and unseen inputs. RAG-based chatbots demonstrate higher accuracy and adaptability, although they require greater computational resources. The study concludes that while RAG-based systems are more suitable for dynamic environments, a hybrid approach combining both methods offers the most effective solution.