Quick Difference
Prompt engineering: Adjust instructions in prompts. Fast, flexible, cheap.
Fine-tuning: Adjust model weights on your data. Slow, specialized, expensive initially, cheap at scale.
Cost Analysis
| Task | Prompt Eng | Fine-Tuning |
|---|---|---|
| Quick testing | — | — |
| Production, high volume | — | — |
| Specialized domain | — | — |
| Cost per inference | — | — |
Speed to Production
Prompt engineering: Hours to days. Iterate, test, deploy.
Fine-tuning: Days to weeks. Prepare data, train, validate, deploy.
When to Use Prompt Engineering
- Need fast iteration
- Unclear data patterns
- Multi-task system
- Compliance or domain rules
- Cost-sensitive at low volume
When to Use Fine-Tuning
- High-volume production
- Specialized domain (medical, legal, code)
- Consistent accuracy needed
- Model behavior (tone, format)
- Cost reduction at scale
Best: Hybrid Approach
Start with prompt engineering (fast, flexible). If accuracy plateau reached, fine-tune. Use fine-tuned model with prompt engineering for domain adjustments.
Sources
- OpenAI. Fine-tuning guide
- Anthropic. Model customization
- Together AI. Fine-tuning vs prompting
Common Mistakes
- Fine-tuning too early (before optimizing prompts)
- Not calculating ROI before fine-tuning
- Using fine-tuning for flexibility instead of accuracy
- Forgetting prompt engineering still matters post fine-tuning