Fine-Tuning Basics — when to reach for it (and when not to)

Fine-tuning = taking an already-trained model and continuing training on a small, focused dataset, so the knobs shift slightly toward your task. Two things make it different from pretraining: the dataset is tiny (hundreds to hundreds of thousands of examples, not trillions of tokens), and the learning rate — how big each knob-nudge is — is set very small. You're sculpting an existing statue, not quarrying new marble. Nudge too hard and you shatter what's already there.

The single most important decision rule in applied LLM work:

Fine-tuning is remarkably bad at reliably injecting new knowledge (Topic on continued pretraining next lesson explains why), and remarkably good at changing behavior: output format, tone, style, domain vocabulary, task reliability. If your problem is "the model doesn't know our product docs" → RAG. If your problem is "the model knows enough but won't consistently output valid JSON in our schema / won't write in our brand voice / wastes 300 tokens of preamble" → fine-tuning territory.

The cost ladder — always climb it in order:

1. Better prompting — minutes of effort. Solves most problems.
2. Few-shot examples in the prompt — show 3–5 examples of what you want. Shockingly effective.
3. RAG — hours to days. Solves knowledge problems.
4. Fine-tuning — days to weeks including data work. Solves behavior/reliability/cost problems.

Jumping straight to fine-tuning because it sounds impressive is the field's most common junior mistake. The legitimate reasons to climb to rung 4: (a) prompting has plateaued below your reliability bar, (b) your prompt has grown into a 4,000-token monster you're paying for on every request — fine-tuning can bake the prompt into the weights, (c) you want a small cheap model to replicate a big model's behavior on one narrow task (distillation, the cost play from Lesson 2), (d) latency demands a small model.

One danger to know from day one: catastrophic forgetting. Train a model hard on only your narrow data and it degrades at everything else — general reasoning, other formats, even chat ability. Like a guitarist practicing one song eight hours a day for a month: that song gets great, everything else gets rusty. Mitigations (details in Module 3): train gently (low learning rate, few passes), mix in some general instruction data with your task data, and use LoRA-style methods that touch fewer knobs.

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