What are embeddings?
Embeddings are numerical representations of text: long lists of numbers (vectors) that capture what a word, sentence or page means. Texts with a similar meaning get similar numbers, so a computer can measure how closely two pieces of content are related, even when they share no words.
An embedding model turns text into a vector with hundreds or thousands of dimensions. In that space, "affordable running shoes" sits close to "cheap sneakers for jogging" and far from "running a small business". Many search systems store these vectors in a vector database and compare them with the vector of a question to find the closest matches.
Embeddings power semantic search, recommendations and the retrieval step of retrieval-augmented generation. You never see them in an AI answer, but they help decide which content the model gets to read.
Why embeddings matter for AI visibility
When an assistant retrieves sources, it often matches the meaning of the question rather than its exact keywords. A page can be retrieved for a prompt it never literally mentions, and a keyword-stuffed page can be skipped because its meaning is vague. Relevance comes down to how well a passage fits the intent behind a question.
This rewards content that covers a topic clearly and specifically. A passage that explains exactly who your product is for, what it does and how it compares sits closer to the questions buyers ask than generic marketing copy. Depth across related subtopics, often called topical authority, gives you more passages that match more questions.
How to optimize content for embeddings
Write one clear idea per passage. A focused paragraph produces a focused embedding that matches a specific question.
Use your customers' words. Describe problems and use cases the way buyers phrase them in prompts, not only in internal jargon.
Name things explicitly. Mention your brand, product category and audience in the passage itself, so its meaning is clear out of context.
Cover the questions around your topic. Pricing, alternatives, integrations and use cases each deserve a clear answer.
Find the gaps. Compare the prompts where competitors appear with the topics your content covers.
Trackbase prompt tracking shows where competitors are mentioned and you are not. Our guide to content gap analysis for AI visibility explains how to turn that into a content plan.
Examples of embeddings
A user asks for "a bike that handles steep hills with groceries on the back". A Dutch bicycle webshop's page about "electric cargo bikes with strong motors for hilly routes" is retrieved, although the wording barely overlaps.
A B2B payroll software vendor's page about "paying contractors in multiple countries" matches the prompt "international freelancer payments", because both embeddings point to the same meaning.
A marketing team converts its blog archive and its tracked prompts into embeddings to spot questions that no article matches closely.
A customer service chatbot uses embeddings to find the right help article when a customer writes "my order never showed up" instead of "missing delivery".
Frequently asked questions
What is the difference between embeddings and keywords?
Keywords match exact words or close variants. Embeddings match meaning, so "cheap flights to Rome" and "budget airfare to the Italian capital" can be recognized as the same request. Many search systems combine both approaches. For your content, keywords still help, but clear explanations of what you offer and for whom carry more weight in AI search.
Can I see the embeddings that ChatGPT uses for my website?
No. AI assistants do not publish the embeddings they use for retrieval, and each system relies on its own models. You can, however, use publicly available embedding models to test how closely your pages match important prompts. The practical takeaway stays the same: clear, specific passages that answer real questions tend to match those questions best.
Are embeddings the same as tokens?
No. Tokens are the pieces text is split into before a model processes it. Embeddings are numerical representations of meaning, created from those tokens. A single embedding can represent a word, a sentence or a whole passage. Tokens determine how much text fits into a model, while embeddings determine how well a passage matches a question during retrieval.