Vector Search and ANN Indexes

Vector search locates the embeddings closest to a query vector. Exact nearest-neighbor runs O(n) per query and will not scale, so production relies on Approximate Nearest Neighbor (ANN) indexes (HNSW, IVF, product quantization) that give up a little recall for enormous speedups. In practice the hard parts are the recall-vs-latency-vs-memory trade-off, metadata filtering, and coping with updates. AI, ML, and GenAI interviews test it because it is the engine beneath RAG and semantic search, and how you tune it directly sets retrieval quality and cost.