@krishnachaytanya: A vector database is how search finds things by meaning, not exact keywords. Every word, doc, or image gets turned into an embedding: a list of numbers that captures its meaning. Search "dog" and it returns puppy, bark, paw, fetch... because their vectors sit closest by cosine similarity. "dot" is spelled almost like "dog" but means something totally different, so it scores low and gets dropped. That is semantic search: rank by distance, keep the top-k nearest neighbors. This similarity search is the memory layer behind RAG and most AI apps (Pinecone, pgvector, Weaviate, FAISS). Would you have guessed "dog" and "puppy" end up as neighbors? Save this for the next time vector DBs or RAG come up in an interview. #vectordatabase #embeddings #rag #semanticsearch #systemdesign

KrishnaChaitanya|SystemDesign

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Thursday 18 June 2026 13:19:38 GMT