The author of the article is a Developer Experience Engineer at Neo4j, who built an educational chatbot for GraphAcademy using Neo4j and Large Language Models (LLMs). The chatbot uses vector search to find relevant sections in the documentation and LLMs to answer user questions based on the context provided. The author emphasizes that providing structured, verifiable knowledge graphs can help prevent hallucinations in LLMs by acting as an external memory or reference source for the model. By utilizing Neo4j's Vector Index feature, the chatbot can find similar documents without relying on external services, making it a convenient way to provide information to users. The article concludes that building such a chatbot is possible and provides a useful tool for GraphAcademy users.