Embeddings

Embeddings are dense numerical vectors that represent the meaning of text, images, or other data. They are the foundation of semantic SEO, vector search, and the entire modern AI retrieval stack.

You do not need to generate embeddings yourself. But understanding what they are, and how they are made, helps you write content that embeddings represent well—and therefore retrieve often.

What an embedding looks like

• An embedding is a list of numbers, typically 384 to 4096 dimensions, that represents a piece of content
• Text that means similar things has vectors that are mathematically close
• Text that means different things has vectors that are mathematically far apart
• The same model that generates embeddings can compare them with a simple dot product

How embeddings are made

• A large language model reads millions of text passages
• For each passage, it learns to predict surrounding context
• The internal representation it builds—the embedding—is the vector
• The same model embeds queries, so queries and documents live in the same space

Why embeddings matter for SEO

• They are how vector search finds your content
• They are how AI engines ground their answers in your pages
• The quality of your embedding representation depends on the quality of your content and its structure

How to write content that embeds well

• Be clear and direct. Embeddings reward clear meaning
• Cover one idea per paragraph. A clean, focused paragraph embeds better than a sprawling one
• Use consistent terminology. A single name for a concept, used throughout, embeds as a single vector
• Use schema markup. Structured data helps models chunk your content into well-defined pieces
• Use natural language. Models trained on natural language represent natural language best