Embeddings and Semantic Similarity: An SEO Primer

Short answer: An embedding is a list of numbers that represents the meaning of a piece of text, so that texts with similar meaning end up close together. Modern search and AI assistants use this idea to match a question with passages by meaning, not only by shared words. For SEO it means writing clear, focused passages that answer one thing well, covering a topic with its natural related ideas, and using similarity tools to find internal link candidates, near-duplicate pages and content gaps.

Embeddings sound technical, but the idea is simple. Computers cannot compare meaning directly, so a model turns a sentence or a paragraph into a long list of numbers. If two texts talk about similar things, their lists are similar, even when the words differ. A search system can then compare a question with thousands of passages and pick the closest ones. This changes how we think about content: the exact keyword matters less than whether a passage clearly expresses the idea that the searcher has in mind. This primer explains the concept without maths, shows how it connects to search, and lists practical uses you can try without a data science team. For the larger picture, see the guide to how AI search engines work.

What an embedding is

Imagine a map where every piece of text is a point. Texts about cooking pasta sit near each other, texts about tax returns sit in another region and texts about running shoes in a third. An embedding is the coordinates of such a point, only the map has hundreds or thousands of dimensions instead of two. A model learns the positions from large amounts of text, so that meaning, context and related ideas shape where a passage lands.

Two points that are close are similar in meaning. The usual measure of closeness is cosine similarity, which compares the direction of the two lists of numbers, giving a score where higher means more alike. You do not need to calculate it by hand; tools do it. What matters is the principle: similarity is measured by meaning, and the words are only a clue.

How search uses meaning

Classic search matched words and counted how often and where they appeared. Modern systems add a layer that understands that a query like how to stop my site from loading slowly is close to a passage about reducing page weight, even if the phrases do not match. Many assistants split content into passages, convert each passage into an embedding, store them and retrieve the closest ones when a question arrives. The retrieved passages are then used to write an answer. The step of splitting is discussed in the guide to passage chunking, and the way a question becomes several searches in the guide to query fan-out.

The exact methods used by each search engine are not public, and they change. It is safe to say that meaning-based retrieval is part of modern search, and that it rewards content in which each passage clearly states what it is about. It is not safe to claim specific ranking effects or scores.

What this means for writing

Because passages are compared by meaning, each section should have a clear focus. A paragraph that mixes three topics produces a muddy representation that matches nothing well. Start sections with a direct statement, then support it, which matches the style in the guide to answer-first introductions. Use headings that describe the content, and keep related facts together instead of scattering them.

Cover the natural neighbours of your topic. A good article about page speed mentions images, scripts, caching and measurement because they belong together, not because a tool listed them. This is the sensible core of the old advice about related terms, and the myth around so-called LSI keywords is addressed in the guide to the LSI keyword myth. For topic coverage as a practice, see the guide to topic coverage and semantic SEO.

Practical use: find internal link candidates

One of the most useful applications for a site owner is internal linking. You can create embeddings for every page or section of your site, then list the pages that are most similar to a given page. Those pairs are natural candidates for links, since they cover related ground. The result is often better than a search for a keyword, because it finds related pages that use different words.

Review the suggestions by hand. Similarity shows relatedness, not value to the reader. Choose links that help someone who is reading the page, with descriptive anchor text, as in the guide to anchor text and the guide to internal linking. Do not insert dozens of links simply because the score is high.

Practical use: find near duplicates and cannibalization

Pages with very high similarity may compete with each other. If two pages answer the same question in similar words, you may want to merge them, differentiate them or point one to the other. Sort the list by similarity and review the top pairs. The same method highlights pages that assistants may treat as copies of each other, a problem described in the guide to duplicate pages and AI citations. For the broader fix, use the guide to keyword cannibalization.

Set a threshold with care. A high score can also result from a shared template, so remove repeated boilerplate before you embed, otherwise every page looks similar to every other.

Practical use: find content gaps and group topics

You can also group the embeddings of your pages and of a list of questions from customers, search queries or support tickets. Questions that sit far from any of your pages indicate gaps. Clusters of pages without a clear parent suggest the need for a hub page or a better structure. The idea matches the method in the guide to topic clusters and the guide to content gap analysis.

The output is a list to think about, not a verdict. A gap may be intentional, because you do not serve that need, and a cluster may be correct even if it looks scattered. Combine the numbers with business judgement.

Keywords still matter, in a different way

Meaning-based search does not make words irrelevant. People still type specific names, codes, model numbers and unusual terms, and systems still need to match them exactly. Many search systems combine both methods: a word-based step that finds exact matches and a meaning-based step that finds related passages. A page that uses the language of its audience, including the precise terms they search for, is served well by both.

What changes is the way you check your work. Instead of counting how often a phrase appears, ask whether a reader who typed the question would find a direct and complete answer in the passage. Read the page as a stranger would, and look for paragraphs that wander. If a section could be pulled out and still make sense on its own, it is likely to work well when a system retrieves it without the rest of the page.

Also think about terminology across your site. If one page calls a feature a workspace, another a project and a third a folder, a person or a model may not understand that they are the same. Choose one term per concept and use it consistently, adding a short explanation of any synonym that readers commonly use.

Limits and cautions

Embeddings are a tool, not a magic ranking button. Different models produce different numbers, so you cannot compare scores between tools. A similarity score does not tell you whether a page is good, accurate or trustworthy. Do not write for a score, and do not try to reverse engineer a search engine from your own model. Quality, accuracy and clear writing remain the base, supported by signals such as authorship and reputation, discussed in the guide to entity SEO.

Think about privacy and cost, too. If you send content to an external service to create embeddings, check the terms and avoid sending private or unpublished material. For a typical site, a modest model run on public pages is enough.

A simple first experiment

Pick one section of your site with twenty to fifty pages. Export the text of each page, remove menus and footers, create embeddings with any available model and list the five most similar pages for each. Review the results for linking and duplication, make a handful of changes and record them. After a few weeks, check whether the pages you improved gained clicks or impressions, using a careful method like the one in the guide to SEO testing. Small experiments build understanding without a large project.

Site SEO AI Audit crawls your website and checks titles, descriptions, headings, duplicate and thin content, broken links, structured data, page speed and AI visibility settings such as AI crawlers in robots.txt, which gives a clean base before you analyse meaning. It does not calculate embeddings or similarity scores. The first audit of up to 200 pages is free, and larger sites can see the pricing page.

The bottom line

Write focused passages that mean one thing clearly, and use similarity to see your own site more clearly.

FAQ

Do I need to calculate embeddings to do SEO?

No. They are a helpful analysis tool, but good SEO still rests on clear content, sound structure and technical health.

Are embeddings the same as keywords?

No. A keyword is a word or phrase, while an embedding represents meaning, so texts with different words can still be close.

Can I improve rankings by raising a similarity score?

Do not write for a score. Scores differ between models and do not measure quality, accuracy or trust.

Which pages should I analyse first?

Start with one section that has many related pages, such as a blog category, and use the results for links and duplicates.

Is it safe to send my content to a service for embeddings?

Only public content, and only after checking the provider terms. Keep private or unpublished material out.