Fine-tuning vs Prompting: When Should You Choose Which?
Compare two approaches to LLM customization — fine-tuning and prompting — with clear selection criteria for each.
Why LLM Customization Is Needed
General-purpose LLMs can handle diverse tasks, but they aren't optimized for specific domains or workflows. Using precise medical terminology, following a company's unique writing style, or consistently generating output in a specific format all require customization.
There are two main approaches: fine-tuning and prompting.
Fine-tuning
Fine-tuning updates the weights of a pre-trained model with additional data. Since it modifies the model itself, it performs the desired behavior without special prompts after training.
When Fine-tuning Is Appropriate
- Consistent output format: Always producing the same JSON structure or specific report templates
- Domain-specific terminology: Specialized fields like medical, legal, or financial
- High-volume repetitive tasks: Processing thousands of identical task types
- Latency minimization: Fast responses needed without long prompts
- Long-term cost reduction: Reducing prompt token costs over time
Limitations of Fine-tuning
- Time and cost for training data preparation (minimum hundreds to thousands of examples)
- GPU resources required
- Retraining needed when model updates
- Risk of overfitting
- Limited for injecting new knowledge (may increase hallucinations)
Prompting
Prompting guides desired behavior through input prompts without modifying the model. It leverages system prompts, few-shot examples, RAG, and more.
When Prompting Is Appropriate
- Rapid experimentation: Test and iterate immediately
- Diverse tasks: Performing multiple task types with a single model
- Current information: Providing real-time information via RAG
- Small-scale projects: Limited training data or investment capacity
- Flexible changes: Only modify prompts when requirements change
Limitations of Prompting
- Long prompts increase token costs
- Context must be provided with every request
- Complex prompt maintenance challenges
- Consistency may be lower than fine-tuning
Comparison Table
| Criterion | Fine-tuning | Prompting |
|---|---|---|
| Initial cost | High (data + GPU) | Low |
| Operating cost | Low (short prompts) | Medium to high (long prompts) |
| Implementation time | Days to weeks | Hours to days |
| Flexibility | Low | High |
| Consistency | High | Medium |
| Latest information | Retraining needed | Instant via RAG |
| Technical difficulty | High | Low to medium |
Practical Decision Framework
Step 1: Start with Prompting
In most cases, prompting is sufficient. First check whether system prompts and few-shot examples can achieve the desired results.
Step 2: Add RAG
If domain knowledge is needed, try RAG before fine-tuning. Retrieving external documents as context satisfies most specialized domain requirements.
Step 3: Consider Fine-tuning
Consider fine-tuning when all of these conditions are met:
- Prompting + RAG quality is insufficient
- Sufficient training data available (500+ examples)
- Repetitive, consistent task types
- Cost/performance optimization is critical
Step 4: Hybrid Approach
Combining a fine-tuned model with RAG yields the best results. The model handles domain style and formatting, while RAG provides current factual information.
2026 Trends
- Fine-tuning democratization: Lightweight techniques like LoRA and QLoRA have significantly lowered costs and barriers to entry
- Prompt → Context Engineering: Evolution from simple prompts to full context design
- Automatic optimization: AI automatically generating optimal prompts or fine-tuning data
Conclusion
Fine-tuning and prompting are not an either/or choice but a spectrum. For most projects, starting with prompting + RAG and gradually introducing fine-tuning as needed is the practical strategy. The most important principle is "try the simplest approach first."