Retrieving information using meaning and context, often alongside exact keyword matching.
Semantic search aims to find information relevant to a query’s meaning and context, rather than relying only on identical words. It can help when the question and the source use different terminology. The term describes an approach to relevance, not one mandatory algorithm.
Embedding-based retrieval is one way to support conceptual matching. A search system can also combine keyword matching with meaning-oriented methods. Exact details such as a part number or quoted phrase may remain essential even when the surrounding question is expressed conversationally.
A relevant-looking result still needs to satisfy the actual task. Semantic similarity does not verify a claim, guarantee freshness or establish that two products are interchangeable. Evaluate whether the retrieved material answers the question accurately, including any conditions that change its applicability.
Takeaway: Use meaning-oriented search to reduce vocabulary barriers, while preserving exact checks where details matter. Test the questions customers actually ask rather than only ideal demonstration queries.
Example
A customer searches a help center for how to change an appointment. The relevant article may be titled rescheduling a booking. A semantic approach can help connect those phrases, but the answer must still reflect the correct cancellation window and booking type.
What to check
Include ambiguous queries, product codes and negative conditions in testing. Review failed matches before changing content or settings. Do not assume that semantic search means keywords and clear page wording have become irrelevant.
For more detail, see Vector search in Azure AI Search.