Question

Your team has built a test set of 500 prompts to evaluate a customer-support summarization feature before each release. Manually reading and scoring every model output takes two engineers a full day, which is delaying releases. The outputs are free-form summaries where correctness is somewhat subjective. Which approach best lets the team scale their evaluation while keeping quality judgments trustworthy?

Accepted Answer

Use an LLM-based grader with a clear rubric to score all 500 outputs automatically, and have humans spot-check a sample to validate the grader's alignment with human judgment.

Answer

Reduce the test set to 20 prompts so the two engineers can finish grading in under an hour each release.

Answer

Skip the evaluation for minor releases and rely on user complaints to surface any quality regressions after deployment.

Answer

Switch entirely to exact-string matching against a single approved summary for each prompt to make grading fully automatic.