OpenAI's text-embedding-ada-002 often gets a peculiar itch at dimension 196—vectors peaking awkwardly there. Enter text-embedding-3-small, swooping in to smooth out the distribution. Now, onto similarity metrics. For unit vectors, the dot product is your fast friend. It's interchangeable with cosine similarity, minus the extra math homework. Vector compression can slim things down with quantization and dimension reduction, but watch out—it might cut corners. Innovative tactics for storage and search can clean up the mess.
