yes. so this is the hardest part — defining the output/results you want the model to tune towards

yes for generative use cases, it's hard to beat openai.  i believe people usually tune for niche/task-specific use cases.

for example: pretrained model was trained on X corpus, but my data set has some variance, and i want tune the model to adjust for the variance

Another I would add is making sure it’s not a problem that can’t already be solved by improving prompts

I’ve held off on fine tuning models because I don’t have a use case that’s specifically terrible with GPT-4

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