How to Evaluate LLM Features Before (and After) You Ship Them
If you can't measure quality, you can't improve it — a practical evaluation playbook for teams building with large language models.
NexusNao TeamEngineering
Vibes don't scale
Most teams evaluate LLM features by trying a few prompts and nodding. That works until the first model upgrade, prompt tweak or unusual user — then quality shifts silently and nobody notices until users do.
An evaluation suite turns quality into a number you can watch. It's the single highest-leverage investment an AI product team can make.
Build a golden set from real inputs
Collect genuine examples of the task — real tickets, real documents, real queries — and define what a good output looks like for each. A hundred well-chosen examples beat ten thousand synthetic ones.
Include the ugly cases deliberately: ambiguous inputs, missing information, adversarial phrasing, edge-of-scope requests. Your users will find them; your evals should find them first.
Score automatically, verify by hand
Structured tasks can be scored exactly. Open-ended tasks can use model-graded rubrics — faithfulness, completeness, tone — calibrated against a sample of human judgments so you trust the grader.
Wire evals into CI. A prompt change that drops groundedness by five points should fail the build exactly like a broken unit test.
Production is part of the eval loop
Sample live traffic into your eval pipeline, track user actions (accepted, edited, rejected) as implicit quality signal, and review flagged failures weekly. The golden set should grow from production reality every month.
Teams that do this ship model and prompt upgrades with confidence in days. Teams that don't stay frozen on whatever worked at launch.