Why did modern LLMs replace LayerNorm with RMSNorm, and why is pre-norm now standard?
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.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
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.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
