This course offers an in-depth exploration of Retrieval-Augmented Generation (RAG) systems, focusing on their practical application in real-world scenarios. By the end of the course, you'll gain expertise in advanced techniques like query expansion, re-ranking, and dense passage retrieval. You'll also understand the core components of RAG systems and learn how to address common challenges in their implementation.

The course begins with an introduction to the basic concepts of RAG, providing an essential foundation for understanding both naive and advanced RAG approaches. You'll dive into the RAG triad and learn about the pitfalls associated with early-stage implementations of RAG, followed by an exploration of more sophisticated techniques. The practical sections will guide you step-by-step through hands-on exercises that involve splitting text, embedding chunks, and performing similarity searches. Advanced topics such as query expansion with generated answers, re-ranking using cross-encoders, and the Dense Passage Retrieval (DPR) technique will be explored thoroughly. You’ll also learn to visualize your results through graph projections and plot embeddings for better interpretation of your data. Throughout the course, you’ll get plenty of chances to apply your learning in hands-on sessions and practical challenges. This course is designed for learners with a foundational understanding of machine learning and natural language processing. It's suitable for professionals and developers looking to master advanced RAG systems for more efficient document retrieval and answer generation. Prior knowledge of Python and machine learning frameworks is recommended.