The minefield — why most fine-tuning quietly fails

Done wrong, fine-tuning looks like it worked and does not. The failure modes are consistent. Catastrophic forgetting: the model gains your task and loses general ability. Overfitting: it memorizes a tiny dataset and falls apart on anything new. Format regression: tuning silently breaks the model's ability to emit clean tool calls — the exact failure that kills agents in production. Data leakage: secrets or PII get baked into weights you cannot un-bake. And the most seductive one, evaluation on training data — the "97% accuracy" that is really the model reciting answers it already saw. Every one of these produces a confident, wrong result that a casual eval will not catch.

This is one piece of a larger framework we built and operate in production. The full picture — and how it applies to your business — is in the playbook.

We specialize in healthcare because it is the hardest vertical — strict HIPAA regulation, PHI handling, BAA chains, and zero tolerance for failure. If we can build it for healthcare, we can build it for any industry. We work across verticals.