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LLM evaluation requires automated metrics to scale beyond manual review
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evaluationmetricsllm-as-judgegolden-setROUGEG-Evalregression
Problem
Teams evaluate LLM features by manually reading outputs and subjectively judging quality. This doesn't scale beyond a few examples and provides no signal for regression detection when prompts or models change.
Solution
Implement automated evaluation metrics: exact match for factual tasks, ROUGE/BLEU for summarization, G-Eval (LLM-as-judge) for open-ended quality, and task-specific correctness checks. Build a golden test set of 50-200 representative inputs with expected outputs. Run evals on every prompt change.
Why
Without automated evals, you cannot detect when a model update or prompt change degrades quality. LLM-as-judge using a stronger model or a specialized evaluator model can approximate human judgment at scale.
Gotchas
- LLM-as-judge is biased toward longer and more verbose responses — use structured scoring rubrics to counter this
- Golden test sets go stale — review and refresh them quarterly
- Eval costs tokens too — design efficient evals that test the failure modes most important to your use case
Context
Maintaining LLM feature quality over time as models and prompts evolve
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