This advanced course provides a deep dive into Retrieval Augmented Generation (RAG) systems, focusing on how modern AI applications retrieve and reason over external knowledge. It is designed for practitioners who want to understand not just how RAG works, but how to optimize it for real-world performance.

The course begins by explaining the fundamentals of vector databases and how they compare to knowledge graphs, helping learners decide which approach best fits different use cases. It then explores effective text chunking strategies and demonstrates how proper chunking directly impacts retrieval accuracy.

A significant portion of the course focuses on embeddings, including whether fine-tuning embedding models improves RAG performance and how optimized embeddings can significantly boost retrieval quality. Learners also examine reranking models and evaluate when reranking truly adds value.

The course pushes beyond text-based systems into multimodal RAG, covering retrieval across text, images, audio, and video. It also introduces domain-specific evaluation techniques to help identify the best RAG strategy for specialized applications.

By the end of the course, learners will have a strong conceptual and practical understanding of modern RAG pipelines, vector search systems, and advanced retrieval optimization techniques. This course is ideal for ML engineers, AI developers, and architects building production-grade RAG systems.