Vector Databases & Embeddings Explained | How AI Search Works

Description

In this video, we explore how AI search works — from embeddings to vector databases and semantic search. Traditional search systems rely on lexical matching (exact words). But modern AI systems understand meaning. Instead of matching keywords, they search based on content, context, and intent. This is made possible through embeddings and vector databases. You’ll learn: • The difference between lexical search and semantic search • What embeddings are and how data becomes vectors • Why AI uses high-dimensional vector spaces • What vector databases actually store • How chunking strategies affect retrieval quality • How similarity search works (cosine similarity, dot product, Euclidean distance) • Why indexing techniques like HNSW and inverted file indexing are needed • What quantization means in vector search • How semantic retrieval powers modern AI systems This video is part of the Tesfa AI Engineering Series, designed to help you understand how real AI systems work behind the scenes. ▶️ Join our Telegram for updates & resources: https://t.me/tesfa_ai