How do you build a data flywheel from production feedback, and what makes feedback loops go wrong?
Production usage can become your best source of training data, or a self-reinforcing trap. What matters is knowing how to capture clean feedback and how to break the loops that quietly corrupt the model.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Production usage can become your best source of training data, or a self-reinforcing trap. What matters is knowing how to capture clean feedback and how to break the loops that quietly corrupt the model.
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.
