AI Scalable Vector Search Explained | Embeddings, Semantic Search & RAG | RAG | Course 13

Description

@LearnAIWithBrema Traditional search looks for keywords. Modern AI systems search for meaning — using embeddings and vector search. In Course 13: Scalable Vector Search Systems, we explain how AI systems store, search, and retrieve knowledge at scale using vector databases, semantic search, and retrieval pipelines — all in simple, easy-to-understand language. This course is a critical foundation for RAG systems, AI agents, recommendation engines, and enterprise AI search. 🔍 What you’ll learn in this course: • What vector search really is • Why keyword search fails for AI systems • What embeddings are (explained simply) • How semantic similarity works • Vector databases vs traditional databases • Indexing, chunking, and retrieval strategies • Approximate Nearest Neighbor (ANN) search • Scaling vector search for large datasets • Vector search in RAG and AI agents • Real-world use cases and system design trade-offs 👥 Who this course is for: • AI beginners curious about RAG & search • Developers and engineers • Data scientists & ML engineers • Product leaders building AI search systems • Anyone working with embeddings or LLMs No prior vector database knowledge required. 📌 If this video helps you: • Subscribe to Learn AI with Brema for more AI masterclasses • Share this video with your friends and colleagues • Like the video to support the channel 🎥 Subscribe here: 👉 http://www.youtube.com/@LearnAIWithBrema 🌐 Free AI courses & structured learning: 👉 https://learn.human-reset-pause.com Learn AI step by step — from fundamentals to scalable real-world systems — with Learn AI with Brema.