layernorm

Two defaults that every Llama-class model shares and almost no candidate can justify. One is a compute win that turned out to cost nothing in quality; the other is what makes a 60-layer stack converge at all.

LayerNorm appears in every transformer, and coding it verifies you know what it normalizes over and why. Interviewers look for normalization across the feature dimension per token, the learnable scale and shift, and the RMSNorm simplification modern LLMs took up.

BatchNorm is one of the most-asked deep-learning questions, and the trap is the train/inference difference. What interviewers reward is what it normalizes, why it stabilizes and speeds training, and why it switches to running statistics at inference. Here is that answer.