How do you choose between human review and model graders?

Instruction: Explain when you would use people versus model-based evaluators.

Context: Checks whether the candidate can explain the core concept clearly and connect it to real production decisions. Explain when you would use people versus model-based evaluators.

Updated

Example Answer

The way I'd approach it in an interview is this: I choose based on the kind of judgment required and the cost of getting it wrong. If the task needs nuanced safety judgment, domain expertise, or interpretation of ambiguous edge cases, I want humans in the loop at least for calibration and critical slices. If the task is repetitive, well-rubriced, and high-volume, model graders can buy a lot of scale.

The mistake is treating this like a binary decision. In practice, the best system is layered. Deterministic checks catch hard failures cheaply. Model graders handle broad coverage. Humans review the slices where subtlety, severity, or uncertainty are highest.

I also continuously compare the graders to humans. A grader that was acceptable last month can drift as prompts, models, and product behavior change. If I stop calibrating, I end up trusting automation more than the evidence justifies.

Common Poor Answer

A weak answer is saying model graders replace humans once the rubric is good enough. For high-stakes or subtle judgments, that confidence is usually premature.

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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