Semantic search

Keyword search asks whether a page contains the words typed. Semantic search asks whether the page means what the person meant. A query for "cheap place to stay near the old town" can match a page about "budget rooms close to the historic centre".

In SEO, "semantic search" also describes how Google has moved, over many years, from matching keywords to understanding topics, entities and search intent. The two uses are related: both mean that wording matters less than whether content genuinely answers the question.

How it works

In AI systems, semantic search usually relies on embeddings. Each document or passage is converted by a model into a list of numbers representing its meaning. The query is converted the same way, and the system returns the passages whose numbers are closest, often using a vector database to do it quickly.

Because meaning is captured approximately, semantic search handles synonyms, rephrasing and questions written in natural language well. It is weaker on exact details, such as a product code, a surname or a specific number, where keyword search excels. Most good systems therefore use hybrid search, combining both and then reranking the combined results with a second model that judges relevance more carefully.

Search engines apply similar ideas at a far larger scale, together with many other signals such as links, freshness and location. That is why a page can rank for queries it never uses word for word. The quality of results also depends on how content is split. A long page covering many topics may be represented by one blurred embedding that matches nothing well, while clear sections with headings give each topic its own sharp match. That is one practical reason structured pages perform better in both site search and AI answers.

Example

A physiotherapy clinic's website search was keyword-based. Visitors typing "my back hurts when I sit" got no results, because no page contained that sentence. After switching to semantic search, the same query returned the clinic's pages on lower back pain and posture at work.

The clinic then checked its search logs. Queries for a particular treatment name still returned weak results, because the name was rare and the model had little sense of its meaning. Adding keyword matching for exact treatment names, alongside the semantic results, solved it. The logs also revealed common questions with no matching page, which became new articles. Six weeks later, searches that ended with no click had dropped noticeably, which the clinic measured in its analytics as a simple before-and-after comparison.

Why it matters for your business

For your website, semantic search can make an on-site search box far more useful, but only if you have enough content for it to matter. For your visibility, the lesson is that writing naturally and covering a topic thoroughly beats repeating exact keywords. Search engines and AI assistants look for pages that answer the question well, whatever words the searcher used. A quick check: search your own site for three questions a customer would ask in their own words, and see whether the right page comes first.

Common mistakes

• Stuffing pages with exact keyword variations when search engines match meaning, not repetition.
• Replacing keyword search entirely, so exact codes and names stop being found.
• Adding semantic search to a site with so little content that results barely change.
• Never reviewing what visitors actually search for on your own site.

Questions people ask

What is the difference between semantic search and keyword search?

Keyword search matches the words in a query to words in documents. Semantic search matches the meaning, so it finds relevant results even when the wording differs. Keyword search is better for exact terms like codes and names, which is why many systems combine both.

Does Google use semantic search?

Yes. Google uses language models and other systems to understand the meaning and intent of queries and pages, not just matching words. Exact words still matter, but a page can rank for queries it never uses verbatim if it clearly answers what the searcher wants.

How do I add semantic search to my website?

Many site search services and some CMS plugins now offer semantic or hybrid search, often configured without code. Custom setups use an embedding model plus a vector database. Start by reviewing what visitors search for, so you know whether better search would actually help.