Originally published on AI Tech Connect.
What you will build Every automated evaluation you run — an LLM-as-a-judge scoring answers, a regression suite gating your deploys, a dashboard tracking quality over time — ultimately rests on a set of human decisions about what "good" looks like. Those decisions live in a golden set: a curated collection of inputs paired with human-verified labels or reference outputs. Get the golden set right and everything downstream becomes trustworthy. Get it wrong and you are optimising confidently towards the wrong target. This guide walks through building the pipeline that produces and maintains that golden set: how to design the labelling task, how to measure whether your annotators actually agree, which self-hostable tools to use, how to feed the pipeline from production traffic, and how to keep…
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