Your classifier outputs probabilities, but you need a decision. How do you pick the threshold (it's rarely 0.5)?
Defaulting to 0.5 leaves money or safety on the table. The right cutoff comes from the cost of each error and the operating constraint, not the model. Here is how to set it deliberately.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Defaulting to 0.5 leaves money or safety on the table. The right cutoff comes from the cost of each error and the operating constraint, not the model. Here is how to set it deliberately.
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.
