Semantic search
Also called vector search, meaning-based search, neural search
Search that finds results by meaning, not just by matching the exact words you typed.
Semantic search returns results that match the meaning of a query, even when they use different words. A search for "cheap places to stay in Lisbon" can find a page titled "Budget hostels in Lisbon" without the word "cheap" appearing anywhere. Traditional keyword search would miss it or rank it lower.
Most semantic search works by embedding both the query and the documents, then finding the documents whose embeddings are closest to the query's. Modern web search engines blend semantic signals with keyword matching, links and many other factors. AI answer engines rely heavily on semantic retrieval to choose which passages to read and quote.
For people writing web content, semantic search changes the advice. Repeating an exact keyword matters less than covering a topic clearly, answering the actual question, and using the natural words readers use. For people building search, the best results usually combine semantic and keyword methods, because exact terms such as product codes and names still need exact matching.
Example
On a help centre, a user types "money back". Semantic search returns the article titled "Refund policy" first, even though the article never uses the phrase "money back".
Common mistake
Replacing keyword search entirely. Semantic search can be poor at exact matches: part numbers, error codes, legal clause references and rare names. Hybrid search, combining both approaches, is more reliable than either alone.