Embeddings and Semantic Search Explained

Traditional keyword search matches exact words, missing a result that uses different phrasing for the same concept.

Semantic search converts both the query and the documents into embeddings, then finds documents whose meaning is closest to the query, even when no words overlap directly.

We use semantic search as the retrieval layer in most AI assistant projects, since it consistently surfaces more relevant results than keyword matching alone.