How do you handle feedback loops and bias in a recommendation system?
A recommender trains on data its own past recommendations produced, so it learns to confirm its own beliefs. What matters is spotting the loop, naming the biases it breeds, and knowing the exploration and debiasing fixes that break it.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A recommender trains on data its own past recommendations produced, so it learns to confirm its own beliefs. What matters is spotting the loop, naming the biases it breeds, and knowing the exploration and debiasing fixes that break it.
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.
