Enhancing Generative AI Chatbot Accuracy Using Knowledge Graph

In recent years, generative AI chatbots have significantly improved in their ability to simulate human-like conversations. However, ensuring the accuracy and contextual relevance of their responses remains a challenge. This paper presents an innovative approach to enhancing the accuracy of generative AI chatbots by integrating knowledge graphs using Neo4j. We demonstrate how combining structured data from Knowledge Graphs with advanced large language models can result in more accurate and context-aware chatbot interactions. By implementing this approach, we aim to provide a robust framework for developing intelligent chatbots that can deliver precise and contextually appropriate responses. We created three categories of test cases: Data-Relevant Inquiries, Non-Contextual Queries, and Contextually Relevant but Data-Irrelevant Questions. The accuracy obtained for the data-relevant test cases was 91.44%.