You suspect your training labels are noisy. How do you detect it and train a good model anyway?
Most real datasets carry wrong labels, and they quietly cap your accuracy. Hand-cleaning all of it does not scale. Here is how to surface the bad labels and train robustly around them.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Most real datasets carry wrong labels, and they quietly cap your accuracy. Hand-cleaning all of it does not scale. Here is how to surface the bad labels and train robustly around them.
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.
