Fine-tuning

Fine-tuning trains a pre-trained model further on your own examples, so a style or skill becomes built in.

• Needs input → ideal output pairs, often hundreds to thousands.
• LoRA trains small added matrices, so it's cheap.
• Too narrow training can make the model forget general skills.

Example: 1,000 support tickets paired with good replies → a model that answers in your company's tone.

Remember: Fine-tune behaviour and format, not fast-changing facts.

Common mistake: Fine-tuning to teach facts that change every week.