A grader model starts drifting after you update the rubric and historical trends become hard to compare. How would you handle it?

Instruction: A rubric change breaks score comparability. Historical raw outputs cannot be retained or regraded under the data policy, but old aggregate scores remain. Explain how you would handle the trend and what a new shared calibration set can and cannot establish.

Context: Tests how the candidate diagnoses the problem, chooses the safest next step, and reasons through recovery. Describe how you would manage grader drift after changing the scoring rubric.

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Version the rubric, grader model, configuration and dataset, and mark a measurement break. Because the old raw outputs cannot be regraded, do not fabricate a historical bridge or shift old scores by an assumed constant. Start a clearly labeled new series and retain the old aggregate series with its original definition.

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Your preparation path

Work through these questions in order. Read the answer aloud, then explain it in your own words.

1. Start with the foundations

Build the vocabulary and explain the core decisions.

  1. What problem should offline evals solve before you ship an AI feature? Free sample
  2. How would you explain the difference between task success and model quality? Free sample
  3. What makes a good regression set for an AI workflow? Free sample

2. Apply it to a real workflow

Practice diagnosis, validation, and everyday tradeoffs.

  1. What signals matter most when evaluating tool-using agents? Free sample
  2. Your tool-using agent passes answer-level evals and still makes bad tool calls. How would you fix the blind spot? Member answer
  3. Production feedback says answers feel less trustworthy even though offline metrics are flat. How would you investigate? Member answer

3. Prepare for senior discussions

Explain failure boundaries, recovery, and production choices.

  1. Human reviewers are expensive, but model graders miss subtle safety issues. How would you balance the two? Member answer
  2. Two coding agents have different scores and sandbox limits. How would you compare them fairly? Member answer
  3. An answer tells your LLM grader to award full marks. How would you keep the evaluation trustworthy? Member answer

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