How you decode from the model's probabilities shapes the output as much as the model itself. The signal is knowing the deterministic vs sampling methods and where each fits (factual vs creative). Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
How you decode from the model's probabilities shapes the output as much as the model itself. The signal is knowing the deterministic vs sampling methods and where each fits (factual vs creative). Here is the answer.
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.
