| Prompt Engineering | VS | Fine-Tuning |
|---|---|---|
| Crafting input prompts to get better outputs from existing LLMs without modifying the model. | Training a pre-trained model on your specific data to specialize its behavior. |
Prompt Engineering
Pros
- Zero cost to implement
- Immediate results
- No ML expertise needed
- Works with any LLM
Cons
- Limited by model capabilities
- Inconsistent across versions
- Can't add new knowledge
- Prompt length limits
Fine-Tuning
Pros
- Deeper customization
- Consistent output quality
- Smaller prompts needed
- Domain-specific expertise
Cons
- Expensive compute requirements
- Needs quality training data
- Requires ML knowledge
- Maintenance overhead
Verdict
Start with prompt engineering — it's free and fast. Fine-tune only when prompts can't achieve the quality or consistency you need.
When to use which
Use prompt engineering for quick wins, testing ideas, and tasks within model capabilities. Use fine-tuning for specialized domains, brand voice, and when consistency is critical.
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