What is semantic search? Definition and how it works

Semantic search is a search method that matches a query to content by meaning and intent rather than by exact keywords, usually by comparing vector embeddings of the query and the documents. It can return a page about "affordable project software for small teams" for the query "cheap tool to organize my team's tasks", even though the two share almost no words.

It is also the retrieval step behind most AI answers, which is why it matters for how brands get found in AI search.

How semantic search works

Classic keyword search, also called lexical search, scores documents by how often and where the query's words appear in them. It is fast and precise for exact terms, but it misses synonyms, paraphrases and intent.

Semantic search adds a layer that works on meaning:

1. Encode. A language model turns every document, or every passage of it, into a vector embedding: a list of numbers that places the text in a space where similar meanings sit close together.
2. Compare. The query is encoded the same way, and the system looks for the passages whose vectors are nearest to it.
3. Combine and rerank. Most production systems mix semantic and keyword scores (hybrid search), then rerank the best candidates with a more precise model.

Search engines also use entities, the people, companies, products and places a text refers to, to understand what a page is about beyond its wording.

Semantic search in AI engines

When ChatGPT, Perplexity or Google's AI features answer with web sources, they do not look up one exact phrase. The engine often breaks the question into several related searches (query fan-out), retrieves the passages that best match each one by meaning, and writes an answer from them. That process is retrieval-augmented generation, and semantic search is its retrieval half.

The consequence for brands is simple: you compete on how well your content covers the meaning of the questions people ask, not on whether you used their exact words.

Why semantic search matters for brands

• People ask in their own words. A buyer asks an assistant about a problem, not about your product category's official name. Content that describes the problem in plain language can be retrieved for it.
• Ranking for a keyword is not enough. A page that ranks for an exact-match term may still not be the closest passage for a longer, conversational prompt.
• Associations add up. When your pages and third-party pages consistently connect your brand with a topic, there is more relevant material for the engine to retrieve when that topic comes up.

How to optimize for semantic search

• Cover the questions around a topic, not just its head term.
• Use the words your customers use, including how they describe the problem.
• Name entities clearly: your brand, products, features and the categories you belong to.
• Write self-contained sections that make sense out of context (see content chunking).
• Link related pages so the topic reads as one connected body of content.

How to measure it

Because semantic retrieval rewards meaning, test your visibility across different phrasings of the same need, not one prompt. In Mencoro you write your prompts or generate suggestions, organize them in groups by topic and read Coverage for each group:

Coverage = checks in which you were named ÷ checks that returned an answer × 100

Example: you track 20 phrasings of "tool to manage team tasks" on one engine, checked once. If the engine names your brand in 5 of the 20 answers, your Coverage for that group is 25%. If it names you for the category term but not for the problem-based phrasings, you know which content gap to close. The methodology page covers how passes and repeated checks are counted, and the free AI visibility checker gives you a first sample.

Common mistakes

• Stuffing synonyms. Listing every variant of a keyword does not add meaning. Answer the question instead.
• One page per keyword variant. Near-duplicate pages compete with each other for the same meaning.
• Vague entities. If a page never states clearly what your product is and who it is for, it is hard to match it to a question about either.

Related glossary terms

• Vector embeddings: the numerical representation that makes semantic matching possible.
• Query fan-out: how AI engines split one question into many searches.
• Retrieval-augmented generation (RAG): retrieval plus generation, the core of AI answers with sources.
• Content chunking: writing passages that can be retrieved on their own.