How should human eval, automated judges, and production metrics work together?
PICTURE THIS: AN LLM TURN
Simple meaning
Humans set the rubric and calibrate judges on a gold subset.
WHY — Tokens instead of words?
LLMs use tokens (not full words) because it helps them:
who only read docs from people who shipped.
and tied to GenAI / LLM work.
Each piece maps to a number the network can learn.
Fixed pieces are what transformers expect as input.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1Humans set the rubric
and calibrate judges on a gold subset.
- 2Why it exists
Automated judges scale daily CI.
- 3Production metrics such as
resolution rate and escalation catch what neither lab set covered.
- 4None of the three
is sufficient alone.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
Humans set the rubric and calibrate judges on a gold subset. Automated judges scale daily CI.