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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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COURSES COVERING THIS TOPIC
No lesson covers this one directly yet. These teach the surrounding topic from the beginning.
Applied AI Engineering·The interviewPremium14mDriving the design conversationA design round is a conversation you are expected to lead, not a question you answer. This lesson is the shape that works, the four moments that decide the outcome, and the two classic ways strong candidates lose one.Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
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