A customer types "my screen keeps going dark" into a support search. Traditional keyword search looks for documents containing those exact words and finds nothing useful. Semantic similarity search converts the query into an embedding, compares it to pre-computed embeddings of every support article, and returns "Display auto-brightness troubleshooting" and "Power saving mode disabling LCD", articles that match the meaning even though they share almost no words with the query. This is the power of similarity search: instead of matching strings, you're matching concepts. It works across languages, handles typos gracefully, and finds connections that keyword search simply cannot.