Design a human-feedback data platform to collect the preference data that trains and aligns your models.
RLHF and evals can only match the preference data behind them, and that data comes from humans whose quality swings wildly. The platform that yields trustworthy labels is a serious system in its own right. Here is its design.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
RLHF and evals can only match the preference data behind them, and that data comes from humans whose quality swings wildly. The platform that yields trustworthy labels is a serious system in its own right. Here is its design.
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.
