News · 2026-08-23 · Sierra Davenport
Fine-tuning is a data problem first
An end-to-end fine-tune guide is useful if you already know what you want the model to stop getting wrong.
Towards Data Science published an end-to-end guide on fine-tuning an LLM. The headline is a how-to, which is the right genre: this is a craft, not a personality upgrade for the model.
The step people skip is still the dataset. If you fine-tune on messy internal mail, you will get a messy internal intern with better grammar. If you do not have evals, you will not know when you made it worse.
Read the guide for the pipeline. Then freeze a tiny eval set from real tasks before you spend a night on adapters.
Takeaway. Write ten failing examples you care about before you touch a training script.