Ground Truth Testing
Ground truth testing compares a model's output against a verified, known-correct answer — established by human experts or an authoritative source — to measure factual accuracy, distinguishing genuinely correct output from merely plausible-sounding output an AI test oracle alone might not catch.
This matters specifically because an LLM can produce fluent, confident-sounding output that's simply wrong — ground truth testing anchors evaluation to an actual, externally verified fact or answer, rather than relying purely on whether the output sounds reasonable, which a model (or even an LLM-as-a-judge) can be fooled by.
Establishing solid ground truth is genuinely labor-intensive — it typically requires subject matter experts to verify or author the correct answers, which is why ground truth datasets are often smaller but more rigorously vetted than broader golden datasets used for general quality checks.
