Asked about Running judges on live traffic
How would you monitor answer quality in production with an LLM judge?
An answer that works
Run validated pass or fail judges for the main failure modes on a random daily sample, plus more of the high-stakes traffic. Track the failure rate over time with an error bar, read the flagged traces, and pin and revalidate the judge model when it changes.
Then they push further
The failure rate jumps from 4% to 9% overnight. What do you check first?
Whether anything changed on either side: a deploy, a prompt edit, a provider model update, a shift in traffic such as a new customer, or a change to the judge itself. Then read the newly flagged traces to see whether the failures are real.
Why not judge every request?
Cost and latency, and it adds little. A sample of a few hundred a day estimates the rate well. Judging everything only makes sense where each individual verdict drives an action, such as holding a message for review.
What gets marked up
Strong answers combine sampling, validated judges, trend tracking and reading flagged traces, and consider the judge itself changing.
Reading an answer is not being able to give one. The section that teaches this is Running judges on live traffic, 4 hours of LLM-as-judge.
Learn this properly