Asked about Linear algebra in NumPy, by doing

What does the dot product of two vectors tell you?

An answer that works

The sum of products of matching entries. Normalised by both lengths it is the cosine of the angle between them, a measure of how aligned they are. It underlies similarity search, attention scores and every layer of a neural network.

Then they push further

Why use cosine similarity for embeddings rather than the raw dot product?

Cosine ignores length and compares only direction, so a long document's embedding does not score higher just because its vector is longer. If embeddings are already normalised to length 1, the two are identical.

Reading an answer is not being able to give one. The section that teaches this is Linear algebra in NumPy, by doing, 5 hours of Linear algebra.

Learn this properly

Also asked about Linear algebra

What does a dot product tell you?Why do matrix multiply shapes have to line up?What is broadcasting and why is it risky?What is the difference between L1 and L2 regularisation?What does SVD give you?How does PCA relate to eigenvectors?How is linear regression a linear algebra problem?Explain PCA.

All 10 questions on Linear algebra