What Are Vector Embeddings and Semantic Search?

What is a vector embedding?

A vector embedding is a list of numbers that stands for the meaning of a piece of text. OpenAI defines an embedding as a vector, a list of floating point numbers, where the distance between two vectors measures how related they are ¹. Small distances suggest high relatedness and large distances suggest low relatedness ¹.

Google Cloud describes an embedding as a type of vector used to represent data in a way that captures its semantic meaning ². 'Semantic' simply means 'about meaning'.

Think of a map. Cafes sit near other cafes, and airports sit far away from both. An embedding places each phrase on a very large map of meaning, so 'cheap hotel' lands near 'budget accommodation' and far from 'tax return'.

What is semantic search?

Semantic search finds results by meaning instead of by matching letters. Elastic defines it as search technology that interprets the meaning of words and phrases, so results match what a query means, not just its literal words ³.

Keyword search looks for the same words, their synonyms or similar words. Semantic search can surface a relevant page even when the query and the page share no words ³.

Elastic's example is 'chocolate milk' against 'milk chocolate'. The words are identical but the order changes the meaning, and a semantic system treats them as different things ³.

What did AI tools search for when we asked about this?

On 20 October 2026 we asked Gemini (with Google Search on) and a Claude agent with a web search tool. Gemini ran seven short searches, such as 'what are vector embeddings' and 'how does semantic search work'. The Claude agent, told to run 6 to 10, ran six and went more practical.

Its searches included 'semantic search vs keyword search difference', 'embeddings cosine similarity content gap analysis tool' and 'semantic SEO mistakes to avoid'. Gemini's answer listed sources from database companies, SEO tools and agencies. One engine and one run each, so this is a sample.

Two needs stand out: a plain definition, and tools or methods for content gaps. This page covers both, and says clearly where no solid evidence exists.

How do embeddings and semantic search work together?

A semantic search system follows three steps. First it converts every document, or piece of a document, into an embedding and stores them. Second, when a query arrives, it converts the query to an embedding. Third, it finds the stored embeddings nearest to the query's and returns those documents in order of closeness ³.

'Nearest' is measured with a distance rule. OpenAI recommends cosine similarity, which compares the direction two vectors point, and says the choice of distance function typically does not matter much ¹. You do not need to calculate it yourself.

The same idea sits inside retrieval-augmented generation, where an AI fetches relevant text before it answers. Our guide to RAG shows how the pieces fit.

Do Google and AI search engines use this?

Yes, in some form, though exact systems are private. Google's ranking systems guide lists neural matching, which it says understands representations of concepts in queries and pages, and passage ranking, which identifies individual sections of a page, as current AI-based ranking systems ⁴.

Perplexity says it combines word-based and meaning-based retrieval, so even a meaning-based engine still uses words too ⁵. Our RAG guide describes its full pipeline.

We do not know whether Google Search uses any specific public embedding product. Google Cloud's documentation describes its own Vector Search service, and says nothing about Search itself ².

What does this mean for SEO and content?

It means write for the idea, not the phrase. Cover the whole question a searcher has, in natural language, and a meaning-based system can connect it to many different wordings of that question.

Google's generative AI guide supports this. It says AI systems understand synonyms and general meaning, and that there is no need to rewrite content only for AI ⁷. It also says there is no requirement to break content into tiny pieces ⁷.

Pages still need clear structure. Because engines pick out passages, each section should hold one idea, open with its answer and name its subject. See our notes on passage ranking and chunking.

What mistakes should you avoid?

The first mistake is stuffing keywords. Google's spam policy defines keyword stuffing as filling a page with keywords or numbers to manipulate rankings ⁶. Meaning-based systems make it even less useful, because repeating a phrase adds no new meaning.

The second is writing vague pages. A page that stays general sits near many topics and wins none. Pick one subject per page and say something specific.

The third is treating embedding tools as a ranking oracle. A tool can show how close your text is to a query in its own model, but no third-party tool has access to Google's internal ranking or AI systems ⁷.

How can you use this idea this week?

You can get most of the value without any code: read your own page against each question. If you want to automate it, embed your sections and the questions with the same model and compare them, as in the table.

• Collect 20 real questions from customers in their own words.
• For each, check you have one section that answers it directly in its first sentence.
• Where a question uses different words from your page, add the customer's wording once, naturally.
• Remove repeated phrases that exist only to hit a keyword.
• Run a monthly test: ask AI tools the questions and see whether your pages are cited. Our Query Fan-Out Explorer shows the searches a model runs.

What an AI assistant searches when it answers this

We gave the question below to AI search tools and recorded the web searches they ran behind the scenes. This is called query fan-out. Each phrase is a page you could publish to be found and cited.

Frequently asked questions

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Sources

1. Vector embeddings (guide) · OpenAIOpened October 20, 2026. Supports: An embedding is a vector of floating point numbers; distance measures relatedness (small distance suggests high relatedness); cosine similarity recommended and the choice of distance function typically matters little. Living page, undated.
2. Vector Search overview · Google CloudOpened October 20, 2026. Supports: An embedding is a type of vector that represents data in a way that captures semantic meaning; dense embeddings use arrays that mostly contain non-zero values. Documentation for Google Cloud's product, not Google Search. Last updated 2026-10-15.
3. What is semantic search? · ElasticOpened October 20, 2026. Supports: Semantic search interprets the meaning of words and phrases; keyword search matches words to words or synonyms; the chocolate milk vs milk chocolate example; query and documents embedded and compared by nearest-neighbour search. Vendor explainer.
4. A guide to Google Search ranking systems · Google Search CentralOpened October 20, 2026. Supports: Neural matching understands representations of concepts in queries and pages; passage ranking identifies individual sections of a page. Last updated 2025-12-10.
5. Architecting and evaluating an AI-first Search API · Perplexity ResearchOpened October 20, 2026. Supports: Documents decomposed into self-contained spans; lexical and semantic retrieval merged into a hybrid candidate set; cross-encoder rerankers. Describes Perplexity's Search API. September 2025.
6. Spam policies for Google web search · Google Search CentralOpened October 20, 2026. Supports: Keyword stuffing is filling a page with keywords or numbers to manipulate rankings. Last updated 2026-08-28.
7. Optimizing your website for generative AI features on Google Search · Google Search CentralOpened October 20, 2026. Supports: AI systems understand synonyms and general meaning, so no need to rewrite only for AI; no requirement to break content into tiny pieces; no third-party tool has access to Google's internal ranking or AI systems. Last updated 2026-07-10.