Beyond keyword matching
Traditional search works by matching the words in your query against the words in a document. Search for "refund policy" and you only find pages containing those exact words. Semantic search converts both queries and documents into embeddings — numerical representations of meaning — and finds matches based on conceptual similarity.
This means "how do I get my money back" finds your returns page even if it never uses the word "money." The search understands the intent, not just the vocabulary.
Where semantic search helps
Knowledge bases: Help customers find answers even when they don't know the right terminology. Essential for RAG-powered chatbots.
Internal documentation: Let your team search across policies, procedures, and training materials by describing what they need rather than guessing at keywords.
Product discovery: "Something to keep coffee warm on my desk" finds your insulated mug even though no product tag uses those words.
Implementation
Semantic search requires an embedding model to convert text into vectors and a vector database to store and search them efficiently. The setup is straightforward with modern tooling, and the quality improvement over keyword search is dramatic for any content where users phrase things differently from how you've written them.