LLM-as-Judge & Human-in-the-Loop
Some outputs are right or wrong — code can check those. But when correctness is a judgment call (Is this summary faithful? Is this reply helpful?), code has nothing to compare against. So you reach for a different judge: an LLM, a human, or both.
Let a Model Grade the Output
Hand a model the output plus a rubric, and ask it to score and critique. It reads nuance the way a person would — at the speed and cost of software.
- Give it the output — the answer you want assessed
- Give it a rubric — what “good” means, in plain words
- Get a score and a reason — nuance at scale, cheaply
- But the judge can be wrong too — it is a model, not an oracle
Making the Judge Reliable
A judge you can’t trust is worse than none. Three things earn that trust: a clear rubric, worked examples, and checking the judge against people.
Keep a Human in the Loop
For quality and for high-stakes calls, people are still the gold standard. You don’t need them on everything — you need them where it counts.
- Label a sample — hand-rate a slice to know the real quality
- Resolve disputes — be the tiebreaker when the automated judge is unsure
- Calibrate the automation — those labels are what you check the LLM-judge against
Cheapest Eval That’s Trustworthy Enough
These three aren’t rivals — they’re a ladder. Use the cheapest one that’s reliable for the job.
- Code — wherever the answer is checkable, it’s free and exact
- LLM-judge — for nuance at scale, once you’ve verified it
- Humans — for the stakes that truly demand them
Build It
How to implement: pick one subjective quality in your tool, write a one-paragraph rubric for it, and have an LLM score a few of your outputs against it. Then read its scores yourself — do you agree? That check is the whole point.
- Weekly AI Tasks tracker — an LLM-judge can rate “did the summary capture the week accurately?” while you spot-check it against a few weeks you know well.
- Personal brand site — an LLM-judge rates tone and clarity; you, the human, make the final call on what’s true and worth including.
What you learned
When correctness is a judgment call, code can’t score it. An LLM-judge gives you nuance at scale — once you’ve made it reliable with a rubric, examples, and spot-checks against people. Humans stay the gold standard for quality and high stakes. Use the cheapest eval that’s trustworthy enough.