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Core
User Feedback Loops and the Data Flywheel
A data flywheel captures implicit and explicit user feedback in production, routes it into eval sets and fine-tuning data, and uses the improved model to attract more usage that generates more feedback. The hard part is not the loop but the signal quality: implicit signals are biased and explicit ratings are sparse and gameable, so naive feedback ingestion teaches the model the wrong thing. Applied AI interviews probe it because a candidate who treats every thumbs-down as ground truth will build a system that degrades while looking like it is learning.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
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