Why do vector databases matter for AI applications?
Embeddings turn text, images, or audio into vectors where similar meaning means nearby vectors. A vector database is built to search millions of those vectors for nearest neighbors in milliseconds — the operation a relational database's B-tree index was never designed for. That's the retrieval half of RAG: turning 'find relevant context' into a fast, structured query.
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20 AI Concepts Every Developer Should Actually UnderstandA layered glossary of the 20 AI concepts developers keep running into — grouped by dependency, not alphabetized, so each term builds on the one before it.
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