# When Fine-Tuning Is the Right Tool — Fine-tuning vs Prompting

Source: https://www.skillbyai.com/en/fine-tuning/d-good

> Recognise the situations where training pays off.

## Style, format, narrow skills, cost and latency

Fine-tuning pays off for **consistent style or format** that a prompt follows only most of the time, **narrow repeated tasks** (classification, extraction, routing) where a small model can match a large prompted one, **shorter prompts** (behaviour learned in training no longer needs long instructions), **lower latency** from a smaller model, and **distilling** a large model into a small one. It works poorly for learning **new facts** reliably, **fast-changing information**, tasks with **few or inconsistent examples**, and anything you cannot evaluate.

## Good and poor candidates

Quick screening before you invest.

```text
GOOD candidates                                          POOR candidates
classify tickets into 12 categories (5k labelled)         answer questions about last week's policy change
extract invoice fields into a strict JSON schema          "be smarter" in general
write replies in our brand voice (1k approved replies)    tasks with 20 inconsistent examples
cut a 3,500-token few-shot prompt to 400 tokens           anything with no evaluation set
distill an expensive model for one task                   a knowledge base that changes daily (use RAG)
```

## Count your examples honestly

Hundreds of consistent, reviewed examples beat thousands of messy ones; if you cannot produce them, stay on the ladder.

**Quiz:** Which task is a good fit for fine-tuning?

- [ ] A task with no way to measure success
- [ ] Answering questions about this morning's news
- [ ] Learning facts that change daily
- [x] Extracting invoice fields into a strict schema with thousands of labelled examples

*Answer:* Extracting invoice fields into a strict schema with thousands of labelled examples. Narrow, repeated, measurable tasks with plentiful examples suit training.