Design a grader stack that mixes deterministic checks, human review, and model-based scoring.

Instruction: Describe how you would combine several kinds of graders into one evaluation system. Required JSON has fields status and evidence_ids, with status limited to supported, partial or unknown. Output is {"status":"success","evidence_ids":[]}. A model grader gives helpfulness 5/5. Decide final status and human-review scope.

Context: Combining deterministic, model, and human grading responsibly.

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I would put deterministic checks first for hard constraints like schema validity, forbidden actions, missing citations, or malformed tool calls...

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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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