Why do vector databases specifically belong in the GenAI stack, and not just any database?
Foundation models don't know your private data — RAG closes that gap by retrieving relevant context at query time. That retrieval depends on finding semantically similar content fast, across potentially millions of embedded chunks, which is what vector databases (Pinecone, Weaviate, pgVector) are purpose-built for. A relational database can store vectors, but nearest-neighbor search at that scale needs indexes designed specifically for it.
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The Generative AI Tech Stack, Layer by LayerFrom GPU infrastructure to model safety, a working map of the nine layers that turn a foundation model into a shippable GenAI product.
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