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How to Evaluate LLM Outputs: The Discipline Behind Reliable AI

An evaluation set — real inputs, graded expected outputs, run on every change — is the single artifact separating production AI from demos, and building it is always the first week of serious work.

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[ BUILD THE SET · 01 ]

50–300 real cases (tickets, documents, queries) with agreed correct outputs, weighted toward the hard and costly ones. Synthetic-only sets flatter the system.

[ GRADE APPROPRIATELY · 02 ]

Grade appropriately

Exact-match for structured outputs; rubric scoring for prose; model-graded evaluation with human spot-audit for scale. Per-field accuracy for extraction, per-intent for conversation.

[ RUN IT ON · 03 ]

Run it on every change

Model swaps, prompt edits, retrieval tweaks — regression-tested like code, because 'it seemed fine' is how quality regressions ship.

[ WIRE IT TO · 04 ]

Wire it to operations

Launch thresholds you approve, dashboards after, drift alerts when accuracy moves — evaluation is a living system, not a report.

Skipping the discipline this article describes until an incident, audit, or stalled project forces it — every practice above is cheaper adopted early than retrofitted under pressure.

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