AI Engineering · LLM applications that ship
How do you use an LLM as a judge safely?
Short answer
Give the judge a narrow rubric, reference material and a structured verdict, then calibrate it against human labels. Use it for scalable grading, not as unquestioned ground truth.
An LLM judge scores outputs against explicit criteria. It scales far beyond human review, but it inherits biases: it may prefer longer answers, its own style, earlier candidates in a pairwise comparison, or confident wording. The judge is a measurement instrument, so it needs its own validation.
- Use a specific rubric with observable criteria, not 'is this good?'.
- Provide the source evidence for groundedness checks; do not ask the judge to verify facts from memory.
- Randomise candidate order for pairwise comparisons and control for length bias.
- Measure judge-human agreement on a labelled sample before trusting it at scale.
- Pin the judge model version and re-calibrate when it changes.
Follow-ups you should expect
Interviewers use these to check whether the first answer was understanding or memorisation.
- 1What agreement threshold would make you trust the judge?
- 2How would you detect position bias?
- 3When is a human panel still worth the cost?
Say it in the interview
I use an LLM judge for scale, but I treat it as an instrument that needs calibration. The rubric has to be narrow and observable, and for groundedness the judge must see the source evidence. I randomise order for pairwise comparisons and check agreement against human labels before trusting it. If the judge and humans disagree, I fix the rubric or fall back to human review rather than optimising against a noisy score.
Use this as a model, not a script. Saying it in your own words is what makes it survive follow-up pressure.
Related questions
Reading is step one
Now say it out loud.
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