In Depth
Semantic search uses AI to understand the meaning of search queries and documents, finding relevant results even when exact keywords don't match. Instead of matching words literally, it converts both queries and documents into mathematical representations (embeddings) that capture meaning, then finds documents whose meaning is closest to the query's meaning. Searching for 'how to fix a broken heart' can return results about emotional coping rather than cardiac surgery.
The technical foundation is embedding models that map text to high-dimensional vectors where semantically similar content is positioned nearby. These embeddings are stored in vector databases for efficient similarity search. Modern semantic search systems typically use transformer-based embedding models fine-tuned specifically for retrieval tasks, such as sentence-transformers, E5, or Cohere's embed models.
Semantic search has transformed enterprise knowledge management, e-commerce product discovery, and information retrieval. It powers the retrieval component of RAG systems, enabling AI assistants to find relevant context from large document collections. For businesses, semantic search dramatically improves findability compared to keyword search, especially for diverse document collections where terminology varies (different departments using different words for the same concept).