Retrieval-Augmented Generation (RAG) enhances the functionality of large language models (LLMs) by integrating real-time data retrieval with generative capabilities. This article guides readers on building RAG systems using the open-source framework Haystack. It covers the stages of retrieval, augmentation, and generation, demonstrating how to set up a RAG pipeline that combines these elements effectively. The article includes practical steps, such as setting up an indexing pipeline and querying for specific information.