Cosine Similarity is the core metric of the new search. It measures the cosine of the angle between two vectors in a multi-dimensional space. In the era of Answer Engines, it determines if your content is “relevant” enough to be retrieved for the user’s query.

If your content vector is orthogonal (90°) to the query vector, you are invisible. If it is parallel (0°), you are the answer.

The Math of Relevance

• 1.0: Identical meaning. The vectors point in the exact same direction.
• 0.0: Orthogonal (unrelated). The vectors are at 90 degrees.
• -1.0: Opposite meaning.

Your goal is not “keyword density” but “cosine proximity.” You want your content vector to sit as close as possible to the Intent Vector, not just the Query Vector.