Mar 24
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Embeddings turn meaning into math. Vector databases make that math searchable at scale. If you're building anything with AI — semantic search, RAG applications, chatbots, or recommendations — embeddings and vector databases are the foundation. This video breaks down both concepts visually without complex math. **What you'll learn:** - What embeddings actually are (and the famous "King − Man + Woman = Queen") - How vector databases make similarity search fast - HNSW algorithm explained - A comon mistake that causes silent failures (mixing embedding models) - Real-world applications: RAG, semantic search, recommendations, multimodal search **Timestamps:** 0:00 - Intro 0:32 - Why Traditional Databases Fail 1:12 - What Are Embeddings? 4:16 - The Vector Database Problem 5:09 - How Vector Databases Work (HNSW) 7:24 - The Critical Mistake 7:50 - Real-World Applications 08:50 - The Complete Mental Model More Videos : Software Egineering Basics - https://www.youtube.com/playlist?list=PLWP-VtjCVpWyLNBm3zz_sGyC5mVwiAOvj Software Design - https://www.youtube.com/playlist?list=PLWP-VtjCVpWx7kPq30XRN6O6LjVQ4VL95 **Resources:** - OpenAI Embeddings: https://platform.openai.com/docs/guides/embeddings #vectordatabase #embeddings #rag #aiengineering #machinelearning