Fine-Tuning vs Prompting: When to Use Which

The Decision Framework

Every AI project faces the same question: Should we fine-tune a model, or can we achieve our goals with prompting and RAG? Here's how to decide.

Start with Prompting

Always start with prompting. It's:

• Fastest to iterate: Minutes to test new approaches
• Cheapest: No training costs
• Most flexible: Easy to adjust for edge cases
• Easiest to debug: You can see exactly what the model receives

When Prompting is Enough

Prompting works well when:

• Your task is well-defined and consistent
• You have clear examples to include
• The base model already understands your domain
• Latency isn't critical (you can use longer prompts)

# Prompting often sufficient for:
- Text summarization
- Simple classification
- Data extraction from structured text
- Translation
- Code explanation

Add RAG When You Need Current Knowledge

RAG (Retrieval-Augmented Generation) adds external knowledge without training:

When to Use RAG

• Your knowledge changes frequently
• You need citations and sources
• The knowledge base is large (>100k documents)
• Accuracy on specific facts is critical

When RAG Isn't Enough

• Highly specialized terminology the model doesn't understand
• Consistent output format requirements the model struggles with
• Performance requirements demand smaller, faster models

Fine-Tune When You Need Behavioral Changes

Fine-tuning modifies the model itself. Use it when:

1. Consistent Style or Tone

If every response must match a specific voice:

# Fine-tune for:
- Brand voice consistency
- Specific formatting requirements
- Domain-specific writing styles

2. Specialized Domain Knowledge

When the model needs to "think" differently:

# Fine-tune for:
- Medical diagnosis reasoning
- Legal document analysis
- Financial modeling logic

3. Performance Optimization

When you need a smaller, faster model:

# Fine-tune to:
- Distill GPT-4 quality into GPT-3.5-size model
- Create specialized models for specific tasks
- Reduce latency for production deployment

The Decision Matrix

| Requirement | Prompting | RAG | Fine-Tuning | |-------------|-----------|-----|-------------| | Quick iteration | Best | Good | Poor | | Current information | Poor | Best | Poor | | Consistent style | Good | Good | Best | | Domain expertise | Poor | Good | Best | | Low latency | Poor | Good | Best | | Cost efficiency | High volume: Poor | Medium | High volume: Best |

Hybrid Approaches

Often the best solution combines techniques:

RAG + Fine-Tuning

Fine-tune a model to be better at using retrieved context:

# Training data format
{
    "context": "[Retrieved documents]",
    "question": "User question",
    "response": "Well-grounded response with citations"
}

Prompting + Fine-Tuning

Fine-tune for core capabilities, prompt for specific tasks:

# Fine-tuned model: Understands your domain
# Prompts: Guide specific task execution

base_response = fine_tuned_model.generate(user_input)
formatted = apply_task_specific_prompt(base_response, task_type)

Cost Analysis

Prompting Costs

• Per-request: Token costs for prompt + response
• Scales linearly with usage
• Long prompts = higher costs

RAG Costs

• Infrastructure: Vector database, embedding computation
• Per-request: Retrieval + generation
• Fixed costs + variable costs

Fine-Tuning Costs

• Upfront: Training compute
• Per-request: Inference (often cheaper than base model)
• Amortized over usage

# Break-even analysis
def calculate_break_even(
    prompting_cost_per_request: float,
    fine_tuning_fixed_cost: float,
    fine_tuned_cost_per_request: float
) -> int:
    """Calculate requests needed to justify fine-tuning."""
    cost_savings_per_request = prompting_cost_per_request - fine_tuned_cost_per_request
    return int(fine_tuning_fixed_cost / cost_savings_per_request)

# Example
break_even = calculate_break_even(
    prompting_cost_per_request=0.02,
    fine_tuning_fixed_cost=500,
    fine_tuned_cost_per_request=0.005
)
# Result: ~33,333 requests to break even

Our Recommendation

1. Start with prompting - Always
2. Add RAG when you need external knowledge
3. Consider fine-tuning when you have:
   • Consistent, high-volume use case
   • Clear quality requirements prompting can't meet
   • Budget for ongoing model maintenance

Remember: Fine-tuned models need maintenance. Data drifts, requirements change, and models need retraining. Factor this into your decision.

Conclusion

There's no universal answer. The right choice depends on your specific requirements, volume, budget, and team capabilities. Start simple, measure results, and add complexity only when needed.