Explain entropy, cross-entropy, KL divergence, and mutual information.
These four quantities sit under cross-entropy loss, decision-tree splits, distillation, and the KL penalty in RLHF. What matters is deriving them from one another and pointing to precisely where each turns up in a real training loop.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
These four quantities sit under cross-entropy loss, decision-tree splits, distillation, and the KL penalty in RLHF. What matters is deriving them from one another and pointing to precisely where each turns up in a real training loop.
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.
