Fine-tuning earns its keep on output format, house style, specialised classification with thousands of labelled examples, and cost reduction, where a small tuned model matches a large general one on a narrow task.
It fails at teaching facts. A fact appearing in a few dozen examples produces a faint, diffuse signal; the model typically learns the form of your documents convincingly while staying unreliable about their content — which is the worst possible outcome.