The author of the article aims to transform the traditional learning process by making it dynamic and engaging through conversational AI. The goal is to revolutionize how people interact with YouTube playlists, enabling active engagement in dynamic conversations inspired by playlist content. To achieve this, the author uses two cutting-edge technologies: LangChain, an open-source framework simplifying the orchestration of Large Language Models (LLMs), and Neo4j, a robust graph database designed for optimal node and relationship traversal. The author processes YouTube playlists using LangChain and Neo4j to extract valuable information from video captions, construct a conversational chain that leads users through a dialogue rooted in playlist content, and perform queries to provide personalized educational dialogues. The article concludes by showcasing the graphical presentation of the conversation history chain using Neo4j Aura database and highlighting the potential of this technology for transforming YouTube learning into dynamic conversations.