Why it matters

Without evaluation, teams debate subjective quality. With it, decisions are data-driven. This is what separates mature LLM teams from novice ones.

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The architecture

Automated metrics: exact match, BLEU, ROUGE, structural correctness (valid JSON, correct schema).

LLM-as-judge: use a strong model to score responses. Consistent and cheap but has biases.

Evaluation methodsAutomatedexact/structuralLLM-as-judgesemantic qualityHuman evalgold standardCombine: automated for regression detection, LLM-as-judge for scale, human for calibration
Three eval levels.
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How it works end to end

Human evaluation: gold standard but expensive. Use for calibration and for tasks where subjective quality matters (creative writing, tone).

A/B testing: compare prompt variants in production. Real users, real outcomes. Slower but authoritative.

Metric design: pick metrics that correlate with real user satisfaction. Optimizing wrong metric wastes effort.