Constructing Chatbots using Generative AI and Domain Knowledge

Abstract

Generative AI powered by large language models has made it possible for chatbots to generate human-like responses; however, major challenges remain in intent recognition, knowledge enrichment, dynamic functionality, and knowledge integration. This paper proposes three design patterns to address these issues: domain adaptation for the retrieval of external information, Intent Bridge for dynamic intent execution, and Knowledge Weaver for the seamless integration of external knowledge. Together, these patterns bridge the gap between theoretical and practical deployment, providing a framework for enhancing chatbot performance in complex tasks. This framework provides developers with systematic methods to improve accuracy, functionality, and contextual relevance in generative AI-powered chatbot systems.