When Ground Truth Gets Dirty

Before you judge, research. Before you trust, question.

Use AI as an exploration tool — ask it for options, probe what’s possible, stress-test assumptions. But don’t treat it as ground truth, especially on non-authoritative topics or anything where the underlying documentation isn’t from a verified source. A simple example: ask AI for the distance between two locations and you may get a confident, wrong answer.

Now here’s the deeper problem: even trusted sources are increasingly using AI to generate documentation. It’s fast, it’s mostly accurate — and that “mostly” is where things start to crack. When accuracy checks shift from thorough to skimmed, errors slip in. Ground truth gets dirty.

And dirty ground truth is dangerous. AI summarizes it. Those summaries get referenced by higher-order AI systems — think memory layers, knowledge bases, orchestration agents. Errors compound quietly until either a hard reset is needed or a root cause analysis so deep it dwarfs the original documentation effort.

This is precisely why Human-in-the-Loop (HITL) isn’t optional for critical processes. And it’s why LLM-as-a-judge should be treated as a supporting layer, not a standalone safeguard.

Trust but verify. Then verify again — while you still can.