Fine-tuning is powerful but expensive. Understand when to use it and when to avoid it.
When to Fine-Tune
DO fine-tune if: Prompt engineering doesn't work, you have domain-specific language patterns, you need consistent output format, you want to reduce hallucinations in a specific domain.
DON'T fine-tune if: The base model already works well with prompt engineering, you don't have quality training data, you're under time pressure, cost is a constraint.
Fine-Tuning Approaches
- LoRA (Low-Rank Adaptation): Efficient, cheaper, good for limited data. Recommended for most cases
- Full Fine-Tuning: Expensive, requires lots of data and compute, better results if you have both
- Instruction-Tuning: Train on task examples, models learn better output format
Data Requirements
You need quality data. 500 well-curated examples beat 5000 mediocre ones.
Format matters. Use consistent prompt/response pairs. If inconsistent, the model learns wrong patterns.
Avoid contamination. Your fine-tuning data shouldn't contain benchmark test sets.
Practical Workflow
- Try prompt engineering first (free and fast)
- If that doesn't work, start with LoRA on a smaller model (cheaper)
- Test thoroughly before upgrading to full fine-tuning
- Track cost vs. improvement carefully
Cost Reality: Fine-tuning GPT-4 costs thousands. Fine-tuning Llama-2 locally costs near-zero. Choose based on your constraints.