Large Language Models (LLMs) have experienced a significant boom. Among the most popular use cases are intelligent knowledge search engines, or put simply - chatbots. Whether on an airline's website, facing customers, or as the new search tool in your company's intranet, chatbots are everywhere. However many applications fall short of expectations. The system used behind many knowledge applications is called Retrieval Augmented Generation (RAG), in which a specific database is connected with an LLM. Many strategies have emerged to enhance RAG performance, but the core is often overlooked—the data itself. In my presentation I will explain RAG as the foundation of intelligent knowledge applications, its pitfalls and caveats. Using a RAG's first step - chunking - as an example, I show what is necessary to improve the reliability and robustness of RAG systems and what you absolutely have to do before you can trust your own chatbot.