Embeddings and vector search: finding what the customer meant

Why keywords miss meaning and vectors miss exact tokens, what an embedding model gives you, why exact search is a memory-bandwidth problem, how HNSW, IVF and product quantisation trade recall for latency and memory, measuring recall@k, filters that silently drop results, hybrid search fused with RRF, choosing and sizing a store for MegaMart Assist, sharding, quiet failures, and changing the embedding model safely.