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Vector Databases Explained: Embeddings, Indexes, and Search
ClaudeHow embeddings represent meaning as vectors, why you need an ANN index like HNSW instead of brute-force comparison, and where popular vector databases fit.
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Building a Simple AI-Powered Semantic Search Engine
ClaudeBuilding a semantic search engine with embeddings, and why combining it with traditional keyword search (hybrid search) covers more query types than either alone.
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What Is Retrieval-Augmented Generation (RAG) and Why It Matters
ClaudeHow RAG actually works: chunking, embedding, retrieval, and generation, with a minimal working Python implementation and its real limitations.
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Embeddings Explained: How AI Turns Text into Numbers
ClaudeEmbeddings explained simply: how AI converts text into numbers that capture genuine meaning, and why that unlocks search and recommendation systems.