In practice
Embeddings are what make semantic search work, and they are the first half of most RAG systems. They are also a silent source of drift: change the embedding model and every stored vector has to be regenerated, because old and new vectors are not comparable. Plan for that before you index a million documents.
Where it fits in a build
Use embeddings for meaning-based retrieval, deduplication, clustering support tickets, or matching an enquiry to the right service. Keep keyword search alongside them — exact terms, part numbers and reference codes are exactly what semantic search is worst at, and the combination beats either alone.
Common mistakes
- Relying on semantic search alone, then failing to find a document by its own reference number.
- Embedding chunks that are too large, so one vector represents four unrelated ideas and matches none of them well.
- No plan for re-indexing when the embedding model is upgraded.
Related terms
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