Embeddings and Semantic Search: What They're Actually For

An embedding turns a piece of text into a list of numbers positioned so that texts with similar meaning end up near each other, even if they don't share any of the same words. That's the whole trick, and it's what makes semantic search possible: searching "how do I cancel my plan" can match a document titled "ending your subscription" even though no word overlaps. Use embeddings for: - Search where the query and the answer are phrased differently (the RAG use case). - Clustering or de-duplicating text by meaning rather than exact match. - Recommending similar items based on content rather than manually-tagged categories. Don't reach for embeddings when plain keyword/full-text search already works — it's cheaper, faster, and easier to debug, and for queries where the exact term matters (an error code, a product SKU, a person's name) keyword search is often more precise than semantic search, not less. Many production systems combine both: keyword search for precision, semantic search as a fallback or a re-ranking signal, rather than picking one exclusively.