Comparison questionMedium~10 minAI Evaluation & Observability / 4 of 18

Interview question

When would you use deterministic checks, model graders, or human review?

Matches exact checks, model judgment, and human expertise to the behavior being measured instead of treating graders as interchangeable.

1Say itAnswer out loud first

2CompareCompare with Core answer

3RefineRefine with Stronger and Advanced

TL;DR

I use deterministic checks for facts the application can verify exactly, such as schema validity, required citations, executable code, or permitted tool arguments. I use a model grader for scalable judgments with a clear scoring guide, such as relevance or whether a response addresses the request. Human experts remain necessary for ambiguous, high-impact, or newly discovered cases and to verify that automated graders still agree with the intended standard.

Full answer below

Practice focus

Evaluator selection, cost, nuance, calibration, and layered scoring.

Answer first

Answer path

Core answer first, then checkpoints toward a stronger answer.

Start
Core answer
Start here. This is the direct spoken answer to practice first.
Overview
The most reliable eval suites combine evaluator types because each one sees a different class of failure.
I use deterministic checks for facts the application can verify exactly, such as schema validity, required citations, executable code, or permitted tool arguments. I use a model grader for scalable judgments with a clear scoring guide, such as relevance or whether a response addresses the request. Human experts remain necessary for ambiguous, high-impact, or newly discovered cases and to verify that automated graders still agree with the intended standard.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
A structured-output workflow might first parse the response, validate its schema, and compare tool calls against allowed behavior before any model judges the explanation quality. Model grading makes broad regression runs affordable, but it can prefer longer answers or repeat the same blind spots as the system being evaluated. Blinded human review is slower, so I focus it on calibration samples, disagreements, risky slices, and changes near a release boundary.
Finish
Advanced answer
Optional deeper angle for production judgment and nuance.
I arrange graders as evidence, not as a voting contest with equal authority. Deterministic contract failures can fail a case immediately, calibrated model scores can cover volume, and expert adjudication can resolve uncertainty or establish new labels. Agreement, disagreement patterns, invalid-grader rates, cost, and latency are monitored by slice. When a model grader drifts or a scoring guide changes, historical scores are not compared as if the measuring instrument stayed constant.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Using an LLM to grade a condition code can verify exactly.
Assuming human reviewers always agree.
Treating a model score as ground truth.

Red flagsCandidateRed flagsSignals that an interviewer may treat as risky.Open details

Details

No automated grader has been compared with expert labels.
An exact contract failure is averaged away.
Reviewer disagreement is hidden from the report.

Follow-up prompts

Quick interviewer prompts inside this drill.

Follow-up questionsCandidateFollow-up questionsLikely next questions after the main answer.Open details

Details

Treat these as the next interviewer turn. Answer one out loud, then save the gap in your practice notes.

What would you grade deterministically?
How large should a human calibration sample be?
What happens when graders disagree?

Continue the section

Next in AI Evaluation & Observability: Calibrate an LLM judge

Step 5 of 18

How would you design and calibrate an LLM-as-a-judge evaluator?

Practice route

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Drill details

Human editorial direction

Aporeon is shaped by Aleksandar Tomovski, a software developer with experience on both sides of technical interviews. Content is reviewed for accuracy, natural spoken delivery, useful depth, and honest trade-offs.

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