A measure of how alike two vectors are by the angle between them; for normalised vectors it's identical to a dot product.
Cosine similarity measures how alike two vectors are by the angle between them, scoring 1 for identical direction and 0 for unrelated. It's the standard way to compare vector embeddings and drive vector search.
A detail we found in Chrome's source code is worth knowing: Google's embedder computes a plain dot product between vectors that have been normalised to unit length. When both vectors have magnitude 1, the cosine formula simplifies exactly to the dot product, so the two are mathematically equivalent — Google just picks the cheaper computation by skipping the division.
The practical point for AI SEO is that "cosine or dot product" is often a non-question: with normalised embeddings they give the same ranking. What matters is that relevance is judged by direction in meaning-space, not word overlap. This underpins how Chrome's history embeddings and similar systems rank content.
