Hybrid search in AI Search
- UpdatedNov 28, 2025
- 3 minutes to read
- Zurich
- AI Search
In hybrid search mode, AI Search blends keyword search and semantic vector search to find knowledge articles, Catalog Items, external content items, and topics that best match the terms and meaning of your search.
Overview of hybrid search
By default, AI Search performs most searches in keyword search mode. This mode looks for the best matches for your search terms, but doesn't take the context or meaning of those terms into account. The keyword search relevance score for a search result indicates how well the indexed terms in that search result match your search terms.
Starting in the Vancouver Patch 4 release, AI Search includes an alternate semantic vector search mode in features which work with the Now LLM Service. Semantic vector search analyzes the meanings and context of your search terms and uses that information to find results with similar meanings. It improves search recall by interpreting natural language to more accurately reflect your search's intent. The semantic vector search relevance score for a search result indicates how closely the search result matches the meaning of your search.
To learn more about semantic vector search mode and how it differs from keyword search mode, see Semantic vector search in AI Search.
Benefits of hybrid search
- Improved search quality and recall
- Hybrid search combines keyword and semantic matching, increasing the chances of retrieving relevant results, even when users phrase their queries differently.
- More relevant top-ranked search results
- Semantic understanding helps surface answers that better match the user’s intent instead of just their search keywords.
- Fewer zero-result searches
- Hybrid search can return useful results even for vague or misspelled queries because it uses semantic matching to understand meaning beyond exact keywords. This reduces the number of searches that return no results.