AI

Embeddings and vector databases quietly power far more than chatbots: search boxes, recommendation feeds, fraud detection, and deduplication pipelines all lean on the same core idea, that meaning can be represented as a point in high-dimensional space and 'similar' can be turned into 'nearby'. This course goes underneath the RAG use case to focus on the mechanics themselves: what an embedding vector actually encodes, how similarity is measured and why the metric choice matters, why brute-force nearest-neighbor search collapses at scale, the approximate algorithms that make billion-vector search practical, and how vector databases are architected differently from the databases you already know. It closes with the practical tradeoffs engineers hit once embeddings leave the whiteboard.