What Are Embeddings? Vector Search Explained in Plain English
The core idea, without the math
Take any piece of text and run it through an embedding model, and you get back a vector — typically a list of a few hundred to a few thousand numbers. Two vectors that sit close together in that numeric space represent text with similar meaning; two that sit far apart mean very different things.
This is why embeddings beat plain keyword matching: the sentences "how much does a website audit cost" and "SEO audit pricing" share almost no words in common, but a decent embedding model places them right next to each other, because it learned meaning, not spelling.
Where you actually run into embeddings
Semantic search (Google, e-commerce site search, internal knowledge bases) uses embeddings to match a query to relevant content even when the exact words don't overlap. Recommendation systems use them to find "similar" products or articles. And retrieval-augmented generation (RAG) — the technique behind most AI chatbots that answer questions about a specific document set — uses embeddings to find the right passage before an LLM writes an answer from it.
This last one matters directly for AI visibility: when an AI assistant answers a question using your site's content, an embedding-based retrieval step almost certainly ran first to decide your passage was relevant enough to hand to the model.
Why this matters for how you write
If retrieval runs on meaning rather than exact wording, a page doesn't need to guess the user's exact phrasing to get retrieved — but it does need each section to be about one clear idea, since an embedding represents whatever chunk of text it's given, and a paragraph mixing three unrelated ideas produces a muddier, less useful vector than one that sticks to a single topic.
Practical takeaway: write self-contained sections with a clear topic sentence, avoid burying the actual answer inside a long tangent, and don't worry about matching a searcher's exact keywords — semantic retrieval is specifically built to look past that.
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