AI & Technical question

Design a closed learning loop for a frontier model: starting from product telemetry, explicit user feedback, and high-severity failure cases, how would you turn those signals into labeled data, evaluation sets, experiments, and post-training priorities while avoiding noisy feedback and overfitting?

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What this question tests

Systems design skill for turning messy real world signals into a disciplined model improvement pipeline.

Define the three input signal types separately: passive product telemetry, explicit user feedback like thumbs down, and curated high severity failure cases from support or red teaming.
Design the triage step: not every signal becomes labeled data, filter for signals that are frequent, reproducible, and represent a real capability gap rather than noise or user error.
Convert triaged signals into labeled data with clear annotation guidelines, and build a held out eval set specifically targeting the failure patterns found, kept separate from training data to avoid contamination.
Run structured experiments, post training runs or fine tunes, against that eval set, measuring both the targeted improvement and regression on existing benchmarks.
Feed results into post training prioritization, ranking failure categories by frequency times severity times fixability.
Guard against overfitting by rotating eval sets and requiring improvement to generalize to a held out sample, not just the specific failure cases that inspired the fix.

Distinguishes triage and filtering from raw signal collection, avoiding the trap of trying to act on every piece of feedback.
Keeps eval sets separate from training data explicitly to prevent contamination and overfitting.
Uses a concrete prioritization formula, frequency times severity times fixability, to rank what gets fixed first.
Requires regression testing on existing benchmarks alongside the targeted fix, showing awareness of tradeoffs.

Treating all user feedback as equally trustworthy signal without filtering for noise.
No mention of held out validation, risking overfitting to the specific failure cases that triggered the loop.

How would you weight a rare but severe failure against a frequent but minor one?
How would you detect if the loop itself was starting to overfit to recent complaints?