Conor Kelly's article explores Retrieval Augmented Generation (RAG) architectures, a technique enhancing large language models (LLMs) by integrating real-time data retrieval. The piece highlights how RAG overcomes limitations like hallucinations, ensuring factual and contextually relevant outputs. It details eight popular RAG architectures, ranging from the simple to more advanced approaches like Agentic RAG, each tailored for different use cases. These architectures offer varied workflows, including memory integration, branched data sourcing, hypothetical document embedding, adaptive strategies, and corrective mechanisms. The article emphasizes RAG's effectiveness in applications like customer support, research, and content creation, where real-time information and accuracy are crucial. The piece concludes by pointing to Humanloop as a tool for enterprises to develop and evaluate RAG-based AI applications.