Vector databases have become a foundational component of modern AI systems — yet many developers struggle to understand why they exist and how they actually work.
In this video, I explain vector databases from first principles using real-world examples, focusing on clarity over buzzwords.
🔍 What You’ll Learn
- Why traditional databases fail at semantic search
- What embeddings are and why they are high-dimensional
- How vector databases enable similarity search using ANN
- How Milvus is architected for scale
- Where vector databases fit in RAG and AI agent systems
🎥 Watch the video here:
👉 https://youtu.be/7hSDxxby66M
This video is part of my AI Foundations for Developers series — aimed at helping software engineers build solid mental models before jumping into frameworks.
📌 Coming next:
LangChain + Milvus, real documents, embeddings, hybrid search, and the foundation for AI agents.
If you’re building or planning to build AI-powered systems, this should help clear a lot of confusion.
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