Beyond RAG: where vector search actually shows up
Retrieval-augmented generation gets most of the attention, but vector search was solving real problems long before LLMs made it popular, and it still shows up far beyond chatbots. Semantic search lets users find documents, products, or support articles by meaning rather than exact keyword match, so a search for 'cheap flights to the coast' can surface a listing titled 'budget airfare to seaside towns' even without a single shared word. Deduplication uses embeddings to catch near-duplicate records, like two customer entries with slightly different spellings, or two nearly identical product listings, that exact string matching would miss entirely. Recommendation systems embed users and items into the same space and recommend items whose vectors sit close to a user's taste profile.
Anomaly detection flags data points whose embeddings sit unusually far from every cluster of normal behavior, useful for fraud detection or spotting defective sensor readings. Image and audio similarity search lets you search 'find images that look like this one' or 'find songs that sound like this one' by embedding the media itself rather than relying on manually written tags. What ties all of these together is the same underlying pattern from earlier lessons: turn something into a vector, then use similarity search to find its neighbors, whether that something is a sentence, a user, a product photo, or a sensor reading.