The digital world is abuzz with AI-powered search enhancements, and at the heart of this transformation lies Retrieval-Augmented Generation (RAG). Think of it as giving your search engine a brain boost – pulling real-time information into large language models (LLMs) to offer you smarter, more comprehensive answers. But how do we build, test, and refine these powerful RAG systems? Introducing Ragnarok, a cutting-edge, open-source framework designed to do just that. Developed for the TREC 2024 RAG Track, Ragnarok offers researchers and developers the tools to explore the exciting potential of RAG. This user-friendly framework streamlines the entire process, from retrieval and reranking to generation, and even features a head-to-head 'battle arena' for comparing different RAG systems. It's not just about frameworks; it's about data too. Ragnarok utilizes a refined version of the MS MARCO dataset (V2.1), minimizing duplicate information and improving the diversity of retrieved content. Plus, it leverages two topic collections: TREC-RAGgy (focused on complex questions) and TREC-Researchy (emphasizing multifaceted inquiries), pushing RAG systems to their limits. But what about real-world performance? Ragnarok provides baselines using industry giants like OpenAI's GPT-4 and Cohere's Command R+, offering valuable insights into how different LLMs handle the challenges of RAG. Initial evaluations reveal fascinating differences, with GPT-4 leaning towards detailed explanations and Command R+ favoring shorter, more concise responses. The project is continuously evolving, with plans to incorporate more advanced techniques and fine-tune the evaluation methods. Ragnarok represents a significant leap forward in the world of retrieval-augmented generation, paving the way for a future where search is smarter, faster, and more insightful than ever before.