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When working with Model Training & Fine-Tuning, the biggest gains usually come from treating it as a repeatable workflow rather than a one-off prompt or experiment. One important area is testing. Define what a good result looks like before changing tools or settings, then test one variable at a...

I think coding and assistants is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. If you share the exact setup, it would be easier to narrow down which part matters most.