Semantic Search: How It Works and Why It Matters

Semantic search is search that retrieves results by meaning rather than by matching the literal words of a query. Most modern systems do it by turning queries and documents into embeddings (lists of numbers that capture meaning) and returning the documents whose embeddings sit closest to the query’s. Google has moved in this direction for more than a decade, and every AI answer engine depends on it.

This is a concept explainer for marketers and site owners who want to understand the machinery. It covers how embeddings and vector similarity work, why most real systems are hybrid, how Google got here from the Knowledge Graph and Hummingbird through BERT and MUM, how AI assistants use it, and what it means for the way you write. The practical method, entity lists and coverage matrices, is in the companion post on semantic SEO.

On this page

• What is semantic search?
• How it works: embeddings and vector similarity
• Hybrid retrieval: why keywords still matter
• Is Google a semantic search engine?
• Meaning-based retrieval in AI assistants
• What this means for your content
• Where semantic search is heading

What is semantic search?

Meaning-based retrieval tries to understand what a query means and return content that answers that meaning, even when the words differ. Google Cloud’s explainer on the topic defines it as “a data searching technique that focuses on understanding the contextual meaning and intent behind a user’s search query, rather than only matching keywords.”

The contrast is with lexical search. Google Cloud describes lexical search as relying “on matching words and phrases based on their literal form without considering their underlying meaning.” A lexical engine sees “how to fix a dripping faucet” and “leaky tap repair” as unrelated strings. A semantic engine sees the same problem. The gap is not academic: when Google launched BERT it said 15 percent of the queries it sees every day are ones it has never seen before, so exact-match lookups cannot cover them all.

That difference matters most for:

• Synonyms and paraphrases: “cheap”, “affordable”, “budget”.
• Ambiguous terms: “jaguar” the animal or the car.
• Long, conversational questions where only a few words carry the meaning.
• Questions with implied relationships: “can a Brazilian traveler enter the US without a visa” depends on who is traveling where.

How it works: embeddings and vector similarity

Most meaning-based retrieval runs on embeddings: a model converts each piece of text into a vector, a list of hundreds of numbers, positioned so that texts with similar meanings end up close together. Search then becomes geometry.

The Sentence Transformers documentation, one of the most widely used open-source libraries for this, describes the core idea: embed all the entries in your corpus into a vector space, then at query time “the query is embedded into the same vector space and the closest embeddings from your corpus are found.” Closeness is usually measured with cosine similarity, the angle between two vectors. The library’s exact search function is meant for corpora of up to about 1 million entries; beyond that, its documentation points to approximate nearest-neighbor methods and search engines such as Elasticsearch.

Three details matter for anyone writing content:

1. Documents are split into chunks first. Systems usually embed passages, not whole pages, because one vector cannot represent a long page with many topics. Your page is retrieved passage by passage. We cover the implications in content chunking for AI.
2. Queries and documents are different shapes. Sentence Transformers distinguishes symmetric search, where query and document are similar in length, from asymmetric search, where a short query must match a longer passage. Web search is asymmetric: a six-word question against a 200-word paragraph.
3. Retrieval is often followed by re-ranking. A fast first pass finds candidates; a slower, more accurate model re-orders them. The documentation recommends this “retrieve and re-rank” pattern.

The diagram below puts the pieces together, including the keyword path most production systems run alongside.

Scroll the diagram sideways to see all of it.

Semantic search vs vector search. People Also Ask for this topic includes “What is semantic search vs vector search?” The short answer: vector search is the technique (find the nearest vectors), and semantic search is the goal (retrieve by meaning). Most of it is implemented with vector search, but entity graphs and language models can do semantic work too.

Hybrid retrieval: why keywords still matter

Pure vector search has blind spots, so most production systems combine it with keyword search. Elastic defines hybrid search as “an information retrieval technique that blends two or more search methods (e.g., lexical search and semantic search) into a single ranked list to improve relevance and recall.”

Each method covers the other’s weakness:

The lists have to be merged somehow. Elastic describes reciprocal rank fusion (RRF) as a method that “ignores raw scores entirely and focuses on how high a document appears in each list,” which sidesteps the problem that keyword scores and similarity scores are on incompatible scales.

For content, hybrid retrieval means the exact terms still matter. If your customers search for a model number, a regulation name or a brand, those strings need to be on the page. Meaning-based matching broadens what can match you; it does not make precise vocabulary optional.

Is Google a semantic search engine?

Google is a hybrid engine with a large and growing semantic component, built up in steps since 2012. The timeline below shows the main milestones Google itself has described.

Scroll the diagram sideways to see all of it.

Knowledge Graph (2012). Google’s move to “things, not strings”, with more than 500 million objects and 3.5 billion facts at launch.

Hummingbird (2013). Rolled out in August 2013 and announced the following month. Search Engine Journal’s history quotes Google’s Matt Cutts saying it affected 90% of searches, “but usually just to a small degree,” with a focus on understanding longer, conversational queries.

RankBrain, neural matching, BERT and MUM (2015 to 2021). In a 2022 overview, Pandu Nayak described each: RankBrain (2015) helped Google understand “how words relate to concepts”; neural matching (2018) “how queries relate to pages”; BERT (2019) “how combinations of words express different meanings and intents”; and MUM, “a thousand times more powerful than BERT,” trained across 75 languages. When BERT launched, Google said it would help with “one in 10 searches in the U.S. in English”, using the example “2019 brazil traveler to usa need a visa,” where the word “to” changes the meaning.

AI Mode (2025). Google’s AI Mode uses what it calls query fan-out, “breaking down your question into subtopics and issuing a multitude of queries simultaneously.” We cover it in Google AI Mode explained.

Google also represents whole pages and sites this way. The 2024 leak of its internal API documentation shows stored page and site embeddings and a site focus score; we explain what that does and does not show in topical authority.

Meaning-based retrieval in AI assistants

AI assistants use this kind of search as their retrieval step: they find passages by meaning, then a language model writes an answer from them. That pipeline is called retrieval-augmented generation, or RAG, and it answers the PAA question “What is RAG vs semantic search?”: the search is one component, RAG is the whole system.

OpenAI documents this for its own tools. Its file search tool “enables models to retrieve information in a knowledge base of previously uploaded files through semantic and keyword search,” using vector stores. That is hybrid retrieval, the same pattern described above. For live web answers, assistants work from a search index and then read the pages they fetch.

The consequence for publishers is that being retrieved and being quoted are separate steps. Semantic similarity gets a passage into the candidate pool; a second judgment decides whether it is used. We cover that gap in retrieval vs citation and the selection signals in how AI engines choose citations.

What this means for your content

This kind of search rewards pages whose passages each carry a clear, complete meaning, and it punishes vague, padded or scattered writing. Five practical consequences:

• Answer in the passage, not across the page. Each section is embedded on its own, so each needs to state its point without relying on earlier sections.
• Name the things you mean. Specific entities, products and standards produce clearer embeddings than “it” and “this solution.”
• Keep exact terms. Hybrid retrieval still matches literal strings, so model numbers, regulations and brand names belong on the page.
• Cover the neighboring questions. Semantic retrieval surfaces your page for many phrasings of related needs, but only if the page actually addresses them. People Also Ask is a good map; see our People Also Ask guide.
• Stop repeating the keyword. Once meaning drives matching, repetition adds nothing but noise.

Scroll the diagram sideways to see all of it.

Turning these principles into a method, with entity lists, coverage matrices and schema, is the subject of semantic SEO. For how your brand itself is represented as an entity, see entity SEO.

Where semantic search is heading

Semantic search is becoming the default retrieval layer for every kind of search interface, from Google’s results page to AI assistants that issue dozens of searches per question. Two trends are clear from what Google and others have published: retrieval is increasingly hybrid, combining meaning with exact matching, and it increasingly works on passages fanned out from a single question rather than on one query and ten pages.

For anyone publishing content, that points in one direction. Write passages that mean something on their own, name things precisely, keep the exact terms your customers use, and cover the questions around your topic, not just the head term.

If you want to know how your pages perform in meaning-based retrieval, and which passages AI engines pick up or pass over, that is part of our AI SEO service.