Most fine-tuning problems start before training begins. You’ll waste time and money if your dataset’s messy, your cost tracking’s nonexistent, or you don’t understand the mechanics of how fine-tuning works.
This guide gives you the preparation toolkit needed for successful fine-tuning. You’ll get dataset format examples, an explanation of how LoRA works, platform research to compare your options, a cost tracking spreadsheet template, and evaluation methods for models without labeled test data. This guide focuses on preparation and planning rather than training code. The planning infrastructure covered here determines whether your fine-tuning project succeeds or wastes resources.