embeddings
Also called: Word embeddings, vector embeddings, text embeddings
Embeddings are numeric vector representations of text (or images, audio) that place items with similar meaning close together in a high-dimensional space. Search and AI systems compare these vectors to match content to a query by meaning instead of exact keywords.
An embedding model reads a chunk of text and outputs a fixed-length list of numbers, often 256, 768, or 1024 values. Each number is a coordinate, so every passage becomes a point in a high-dimensional space. Text about the same topic lands in the same neighborhood, even when the actual words differ.
How systems use them
To match a query to content, the system embeds the query, embeds candidate passages, then measures the angle between the two vectors with cosine similarity (a score from -1 to 1). Higher score, closer meaning. That is why a page about “trainers for jogging” can surface for “running shoes” with zero shared keywords.
The idea traces to Google’s 2013 word2vec paper, which showed that words used in similar contexts end up near each other in vector space. Early models gave each word one fixed vector. Modern transformer models produce context-aware embeddings, so “bank” in a finance sentence and “bank” by a river map to different points.
For search, this is the retrieval layer. AI Overviews, ChatGPT search, and Perplexity pull a set of candidate passages by embedding similarity before any re-ranking or quality filtering happens. If your passage does not embed close to the questions your buyers ask, it never enters the shortlist, and no amount of on-page keyword tuning changes that.
How it affects your traffic
Embeddings decide whether your pages get considered by AI answer engines at all, so keyword stuffing does nothing here; semantic clarity does. Pages that state one clear idea per passage, in the vocabulary your customers actually use, embed close to real queries and get pulled into AI answers and semantic search results. When AI systems keep skipping your content, the fix is usually structural rather than cosmetic, and that retrieval-focused work is what our AI SEO service handles.
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