What it means
Queries and documents live in the same embedding space; the system returns items closest in meaning to what the user asked.
Why designers should care
Semantic search needs UX for ambiguity: query refinement, faceted filters, and previews of why a result matched, especially in enterprise knowledge bases.
Example
A design system search for “make destructive actions obvious” surfaces Danger button, error banners, and deletion modals, even if none contain the word “destructive.”
Common mistakes
- Single-result answers with no list to verify the right doc was found.
- Ignoring metadata filters (team, product, locale) that embeddings alone cannot infer.
- Over-trusting top result without snippet highlighting.
Frequently asked questions
What is Semantic Search?
Semantic search finds content by meaning and intent, not just exact keyword matches, powered by embeddings and vector comparison.
Why should designers care about Semantic Search?
Semantic search needs UX for ambiguity: query refinement, faceted filters, and previews of why a result matched, especially in enterprise knowledge bases.
What are common mistakes with Semantic Search?
Single-result answers with no list to verify the right doc was found; Ignoring metadata filters (team, product, locale) that embeddings alone cannot infer; Over-trusting top result without snippet highlighting.