Fine-tuning tutorials just turned model training into paint-by-numbers
Step-by-step fine-tuning guides are creating a generation of practitioners who can follow recipes but can't cook.
We’re drowning in fine-tuning tutorials. Every day brings another complete step-by-step guide promising to turn anyone into a model training expert. The problem isn’t the tutorials themselves. It’s that they’re creating practitioners who know how to execute but don’t understand what they’re executing.
Following recipes isn’t cooking
These guides teach parameter adjustment without understanding parameter interaction. They show you how to set LoRA ranks and learning rates but skip why those numbers matter. QLoRA becomes a magic incantation rather than a memory optimisation technique. DPO gets treated as just another config file setting.
The result is a cottage industry of fine-tuned models that barely improve on their base versions. We see endless variants trained on the same datasets with identical hyperparameters because that’s what the tutorial recommended.
Understanding beats implementation
The practitioners who actually move the field forward don’t start with tutorials. They start with papers, experiment with assumptions, and break things systematically. They understand the mathematical foundations well enough to know when to ignore best practices.
Tutorial-driven development creates competent implementers but terrible innovators. When everyone follows the same playbook, we get incremental improvements instead of breakthrough techniques. The next major advance in fine-tuning won’t come from better tutorials. It’ll come from someone who understood the fundamentals well enough to throw the playbook away.