Fine-tuning is supposed to be the polite part of AI customization. A company uploads domain data. A provider adapts an aligned model. The final model still refuses harmful requests, still answers useful questions, and ideally becomes more competent at the client’s narrow task. Everyone nods. The demo works. The governance slide says “safety preserved.” The slide, as usual, is doing a lot of unpaid labor. ...
Failure logs are usually treated as evidence for the prosecution. A model is asked to produce a concise compliance summary with three bullet points, mention two risks, avoid prohibited claims, and end with a recommendation. It produces three bullets, correctly identifies the risks, avoids the prohibited claims—and forgets the recommendation. Under a strict binary reward, the response receives a zero. Under a partial-credit reward, it might receive 0.75. The first signal says nothing useful happened. The second says something useful happened, but not precisely what. ...
The prompt was clear. Then the conversation kept going. A familiar enterprise AI story starts politely enough. The legal assistant is told to be conservative. The medical triage bot is told not to diagnose. The procurement agent is told never to approve a vendor without documented checks. Everyone nods. The system prompt is immaculate. Compliance is laminated. ...