In this channel's material, ground truth is what separates guessing from evaluating. Jared Zoneraich frames the choice directly: when you backtest a new prompt against your last thousand or ten thousand responses, the signal that it changed everything is not useful on its own — with ground truth, you anchor on real metrics and AB tests instead of eyeballing diffs or chasing similarity scores. [^d351f8cb-2125-5ece-8488-6f6ee66c9ee2@1791] The material then shows what to do when ground truth doesn't exist. For tasks like email summarization, Zoneraich recommends dissecting your own intuitive judgment into explicit heuristics — killing the “This summary is about…” preamble, fixing markdown indentation — or handwriting the output you wished you'd gotten and extracting principles from it. [^d351f8cb-2125-5ece-8488-6f6ee66c9ee2@2063] That handwritten ideal effectively becomes the ground truth you measure drafts against. Michael Taylor's workflow automates the same move: give the model a good and a bad version, ask what differs, and turn each difference into an evaluation criterion. [^d351f8cb-2125-5ece-8488-6f6ee66c9ee2@2063] Explore both paths and you'll see the pattern: ground truth is the anchor you either already possess (real metrics, AB tests) or must deliberately construct (heuristics, handwritten ideals, good-vs-bad comparisons) before an eval can tell you anything reliable.