Understanding LLM Embeddings from Scratch — Part 2: Vectors, Dimensions, Dot Products & Cosine Similarity

In Module 1, we learned that an embedding layer converts a token ID into a vector. For example: cat = [0.90, 0.10] dog = [0.85, 0.15] We said these two vectors look similar, but we haven't explained w