How does an AI understand that a buying agent and a housing adviser have something to do with each other, even when those words never appear together? The answer lies in embeddings: the technique with which AI turns meaning into numbers and so sees connections that are obvious to us.
What are embeddings?
An embedding is a mathematical representation of meaning. AI systems convert words, sentences and whole texts into a series of numbers that captures their meaning. Concepts that are close in meaning get similar number sequences. This creates a kind of map of meaning, in which related topics lie close together.
Why is this relevant?
Embeddings explain why semantic SEO works. A page that treats a topic in its full breadth gets a rich, recognisable position on that map of meaning. An AI then effortlessly recognises what the page is about and how it relates to a question being asked.
An example
Say someone asks an AI for help buying a home. The question may not contain the exact words of your page. But if your content is close in meaning to that question, the system recognises that proximity through the embeddings and can consider your page relevant.
What does this mean for you?
You no longer have to guess your audience’s exact keywords. You have to genuinely understand the topic and cover it fully. Then the map of meaning created by embeddings ensures you are found for the questions that fit it, even when the words are slightly different.
