May 6
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Ever wondered where AI embeddings actually live? This episode breaks down vector databases — what's inside them, how they index your vectors, and which one to choose for your project. Key Takeaways: • Vector databases aren't just search — they handle storage, filtering, replication, and APIs around HNSW indexing • Metadata filtering is the hardest problem in vector DBs — how your database handles filtered search should drive your choice • Start with Chroma for prototyping, graduate to Qdrant for production — you'll outgrow Chroma, but it's the fastest way to start 🔔 Subscribe for more AI engineering episodes — we're covering everything from tokens to production deployment in 98 episodes! 💬 Drop a comment: Which vector database are you using in your projects? 👍 Like this video if you learned something new! #VectorDatabase #AIEngineering #RAG #Embeddings #MachineLearning