How do you detect hallucinations in LLM output (as opposed to preventing them)?
Preventing hallucinations is one job; catching the ones that slip through at runtime is a separate one. The signal is the detection toolkit, why each signal is imperfect, and how you merge them into an action.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Preventing hallucinations is one job; catching the ones that slip through at runtime is a separate one. The signal is the detection toolkit, why each signal is imperfect, and how you merge them into an action.
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.
