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Fine-tuning & Customization

Fine-tuning is the tool people reach for too early. This is the engineer's version of the decision: when a prompt or RAG already solves it, when fine-tuning is genuinely worth the cost, and how the machinery actually works — supervised fine-tuning and LoRA, preparing clean labeled data, distilling a small fast model from a big one, and evaluating a tuned model on held-out data before you trust it. Six short lessons, every screen drawn, no hype.

6 lessonsIntermediate35 min

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Course lessons

Prompt vs RAG vs fine-tune

The decision tree every engineer should run before reaching for fine-tuning.

How fine-tuning works (SFT, LoRA)

Supervised fine-tuning nudges the model toward your examples — and LoRA does it cheaply.

Preparing the data

Fine-tuning is only as good as the examples you feed it — quality beats quantity.

Distillation

Use a big model to train a small fast one that keeps most of the quality.

Evaluating a tuned model

A tuned model can look great on its demos and be worse everywhere else — measure honestly.

Putting it together

A quick recap — and the one rule that keeps you out of the fine-tuning trap.

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