What Are Vector Embeddings? A Visual Guide for SEO Professionals

A vector embedding is a list of numbers that represents a piece of text in a way that captures its meaning. Instead of treating words as arbitrary symbols, embedding models convert text into points in a high-dimensional mathematical space where similar meanings are close together.

How Vector Embeddings Work

The process involves tokenisation (breaking text into sub-word units), neural network processing through a transformer architecture, and output as a dense vector with 768 to 3,072 dimensions.

Measuring Similarity

Cosine similarity measures the angle between two vectors. A score of 1 means identical meaning, 0 means unrelated. The classic example: king - man + woman ≈ queen.

Vector Embeddings in Search

Modern search engines use embeddings for query understanding, document retrieval, re-ranking, and AI Overviews/RAG systems.

SEO Implications

• Semantic coverage matters more than keyword density
• Topical authority builds stronger embeddings
• Structure helps embedding models extract meaning
• Entity clarity improves semantic matching