Fine-Tuning
Re-training an existing LLM on your own data to teach it a specific style, domain, or format. More powerful than prompt engineering for very targeted cases, also much more expensive.
Strengths
- Very precise results for very targeted use cases
Limitations
- High cost and rigidity, any data update requires a new fine-tuning run
Best for
- Understanding why fine-tuning is generally not the first solution to try