In this episode, David and Thomas discuss their use of a large language model to build a chatbot based on documentation. They explain the concept of prompt engineering and how it can be used to guide the model's responses. They also introduce the concept of retrieval augmented generation (RAG). They also discuss the use of knowledge base articles and documentation to influence the behavior of LLMs. Additionally, they touch on the use of graph databases and embeddings to identify redundancies in documentation and improve search results. The conversation concludes with a discussion on the challenges and limitations of working with LLMs.