> Fine tuning is better for complex tasks where the model’s generated output must be accurate and trusted. uhhh. I understand what was intended there but whil

> Fine tuning is better for complex tasks where the model’s generated output must be accurate and trusted. uhhh. I understand what was intended there but while fine tuning may reduce the rate of hallucinations and make hallucinations more plausible, it's not magic accurate and trust-worthy dust. Unfortunately many people think this stuff is magic and care should be taken to not encourage people to confuse improvements with resolving the issue. One way of characterizing the LLM accuracy problem is that it often looks very accurate and convincing even when it is emitting nonsense. If you cast the problem in those terms-- as a problem of looking more trustworthy than it actually is-- fine tuning actually exacerbates the problem.