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Cosine Similarity Explained for High-Dimensional Vectors
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Ethan
December 25, 2025
Summary
Cosine similarity measures how aligned two vectors are, not how big they are—and that’s why it works so well for embeddings. In this video, I explain:
short-form
About this video
Cosine similarity measures how aligned two vectors are, not how big they are—and that’s why it works so well for embeddings. In this video, I explain: * How data becomes points in high-dimensional space * Why cosine similarity focuses on direction, not magnitude * How this scales from 2D intuition to thousands of dimensions * Why cosine similarity is the default for semantic search This is the simplest way to understand how modern AI compares meaning.