What you'll learn

Traditional "exact match" search tools often fail to capture human intent, leaving organizations struggling to find relevant information within vast amounts of unstructured data like PDFs and large text fields. In this course, Introduction to Vector Databases, you’ll gain the ability to build and architect context-aware retrieval systems that power modern AI applications. First, you’ll explore the fundamentals of embeddings and distance metrics to understand how computers represent and measure semantic meaning. Next, you’ll discover the core mechanics of vector databases, including Approximate Nearest Neighbor (ANN) indexing and metadata filtering for high-performance retrieval. Finally, you’ll learn how to implement practical AI workflows like Retrieval-Augmented Generation (RAG) and recommendation systems. When you’re finished with this course, you’ll have the skills and knowledge of vector databases needed to transition from rigid, keyword-based systems to proactive, intelligent AI retrieval.