GlossaryAI search
What are embeddings?
Also called: vector embeddings, text embeddings, embedding model
Definition
Embeddings are lists of numbers (vectors) that represent the meaning of a piece of text. Texts with similar meaning get vectors that sit close together, which lets software search by meaning instead of exact words.
Embeddings, explained
OpenAI's documentation puts it simply: an embedding is a vector, a list of floating point numbers, and the distance between two vectors measures their relatedness. Small distances suggest high relatedness, large distances low relatedness. An embedding model reads a sentence, paragraph or page and outputs one of these vectors.
The useful property is that meaning survives different wording. "How do I cancel my plan" and "stop my subscription" share few words but land near each other. That's why embeddings power semantic search, recommendations, clustering, duplicate detection and the retrieval step in RAG.
For AI search, embeddings are one of the ways a system decides which passages are relevant to a question. It embeds the query, compares it with embeddings of candidate passages, and keeps the closest ones. Many systems combine this with classic keyword matching, because embeddings can be fuzzy about exact names, numbers and product codes.
Two practical consequences for content. First, keyword stuffing does little for an embedding, because the vector reflects overall meaning, not repetition. A passage that clearly explains the thing someone asked about is closer to the question than one that repeats the phrase ten times. Second, a passage that mixes three topics gets a blurry vector that's not especially close to any single question. Focused sections tend to retrieve better.
Embeddings are also handy for your own SEO work. You can embed every page on your site and find near-duplicates before Google does, or cluster keywords by meaning when planning topics. That's the kind of similarity check that belongs in a publishing workflow's quality gates.
Why it matters for founders
Embeddings are why AI search can match a buyer's question to your page even when you never used their exact words. They also give you a cheap way to catch duplicate and overlapping pages before publishing.
Example
A founder embeds all 300 blog posts and finds 14 pairs with near-identical vectors. They merge each pair into one stronger page and redirect the weaker URL, before Google has to choose between them.
Common mistakes
- Repeating a keyword to "match" semantic search. The vector reflects meaning, not frequency.
- Cramming several topics into one section, which blurs what it's about.
- Relying on embeddings alone for exact matches like product names, versions or prices.
Sources
Checked
Related terms
- Vector searchVector search finds content by meaning. It turns the query and every document into embeddings, then returns the documents whose vectors are closest to the query's, instead of matching exact words.
- Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) is a technique where a system first retrieves relevant documents, then passes them to a language model so it can answer from that material instead of memory alone.
- Content chunkingContent chunking is structuring a page as self-contained sections, each answering one question, so search engines and AI retrieval systems can find, understand and quote a single passage without the rest of the page.
- Keyword stuffingKeyword stuffing is filling a page with keywords or numbers in an attempt to manipulate search rankings, often as unnatural repetition, lists of places or terms, or text out of context. Google's spam policies prohibit it.