What is Semantic Search?
Semantic search is an information retrieval method that attempts to find results by analyzing the meaning, intent, and context of a query, rather than relying solely on matching exact keywords.
How does it work?
Semantic search uses AI to convert both the search query and the documents into mathematical vectors called embeddings. When a user searches, the system calculates the distance between the query's vector and the document vectors. Documents that are mathematically close are returned as results, even if they share no words in common with the query.
What is it commonly confused with?
Do not claim that semantic search fully understands meaning the way a human does, or that it always outperforms keyword search. For finding a highly specific serial number, keyword search is better. Modern systems often use hybrid search, combining both semantic meaning and exact keyword matching.
Why does it matter?
Semantic search powers modern enterprise AI and Retrieval-Augmented Generation (RAG). It allows users to ask natural language questions and receive accurate answers from company documents without having to guess the exact phrasing the original author used.