Semantic Search

Learn what actually changes in your retrieval stack when you move from keyword search to embeddings and vector indexes, and why hybrid search is usually the right answer.

A vector search handed an agent a 0.89 confidence score and the wrong function — here's why embeddings can't tell 'looks like auth' from 'is auth,' and what to check before an agent acts on either.

A cosine similarity score feels like proof of quality — until an exact SKU search comes back empty, and you realize 'semantic' was never something you'd actually measured.