This course contains the use of artificial intelligence. Large Language Models are powerful, but on their own, they lack access to your private data and cannot be trusted to answer reliably. This is where Retrieval-Augmented Generation (RAG) becomes essential. In RAG 101: Build AI Systems with Your Own Data, you will learn how to design and build reliable, end-to-end RAG systems from scratch, with a strong focus on intuition, architecture, and real-world best practices.
This course starts from first principles and gradually takes you to advanced RAG architectures. Every concept is explained clearly: not just how things work, but why they work. You will begin with chunking fundamentals, learning how to split documents effectively and how chunk size and overlap directly impact retrieval quality. You will then dive into embeddings, understanding how text is transformed into vectors and how semantic similarity actually works.
Next, you will explore vector databases, including indexing strategies, metadata usage, and how modern vector stores power fast and accurate semantic search. From there, you will master the retrieval phase, covering similarity metrics, static and dynamic Top-K strategies, filtering, and advanced techniques such as MMR, BM25, and reranking.
