What is Semantic Search?
Search technology that understands the meaning and intent behind queries rather than just matching keywords, delivering more relevant results by grasping context and relationships.
Understanding the Details
Traditional keyword search finds documents containing exact word matches. Semantic search understands that 'how to reduce customer churn' and 'preventing users from cancelling' mean the same thing, even though they share no keywords. This works by converting text into numerical representations (embeddings) that capture meaning, then finding content with similar meaning to the query. For product search, this means users find what they're looking for even when they don't use the exact right terms. For internal knowledge bases, it means employees can ask natural questions and get relevant answers. Semantic search is the foundation of RAG systems, where AI retrieves relevant context before generating responses.
How It Works in Practice
Help centre search
A customer searches 'can't log in' and gets results about password resets, SSO configuration, and account lockouts — understanding intent rather than just matching keywords.
Product catalogue
Searching 'comfortable shoes for standing all day' returns relevant products even though no product description contains that exact phrase.
Internal knowledge base
An employee asks 'what's our policy on remote work?' and finds the relevant HR document titled 'Flexible Working Arrangements'.
Why It Matters
Users increasingly expect search to understand what they mean, not just what they type. Poor search creates friction that drives people away from your product or content.
What People Often Get Wrong
Semantic search replaces keyword search entirely. Actually, hybrid approaches combining both often produce the best results.
Semantic search is always more accurate. Actually, for exact-match queries like product codes or names, keyword search can be more precise.
Implementing semantic search is simple. Actually, it requires embedding models, vector storage, and careful tuning for your domain.
How We Handle Semantic Search
We implement semantic search using vector databases and embedding models tuned for your domain, often as part of RAG systems that power AI-assisted features.
Related Terms
Common Questions
Need Help With Semantic Search?
If you'd like to discuss how semantic search applies to your business, we're happy to explain further.