Fine-Tuning vs Prompt Engineering: When to Use Each
Quick Answer
Start with prompt engineering. Only fine-tune when prompts aren’t enough.
Prompt Engineering
When It Works
- General task improvements
- Format control
- Few-shot learning
- Role assignment
When It Doesn’t
- Consistent style across many examples
- Domain-specific knowledge needed
- Need better base model behavior
Cost
- Time only (no API cost increase)
- Iterative experimentation
Fine-Tuning
When It Works
- Consistent style/voice
- Domain-specific tasks
- Reducing hallucinations
- Cost optimization (smaller model = cheaper)
When It Doesn’t
- General knowledge tasks
- Need latest model capabilities
- Limited training data
Cost
- Training: $50-500+ (one-time)
- Inference: Can use smaller model
Decision Framework
| Need | Approach |
|---|---|
| Better formatting | Prompt engineering |
| Specific style | Fine-tune |
| More knowledge | Fine-tune |
| Tool use | Base model |
| Lower cost | Fine-tune small model |
| Latest model | Prompt engineering |
Our Recommendation
- Start: Prompt engineering
- Iterate: 20+ iterations before giving up
- Fine-tune: If prompts hit ceiling
- Re-evaluate: As models improve
Most teams don’t need fine-tuning.