Prompting vs Fine-Tuning: Which to Choose?
Most agents only need good prompts. Here's how to know when you've outgrown prompting and need fine-tuning.
The Quick Answer
Start with prompting. Fine-tune only when necessary.
90% of agent use cases work great with well-crafted prompts.
What Is Prompting?
Instructions given to the model at runtime:
- System prompts define behavior
- Few-shot examples show patterns
- Context provides relevant information
Pros: Fast iteration, no training cost, flexible.
Cons: Limited by context window, can't learn new patterns.
What Is Fine-Tuning?
Training the model on custom data:
- Updates model weights permanently
- Learns from examples at scale
- Specialized for specific tasks
Pros: Better performance, smaller prompts, consistent style.
Cons: Expensive, slow iteration, frozen after training.
When to Use Prompting
- Standard tasks (summarization, extraction)
- Prototyping and experimentation
- Tasks with clear examples
- Budget-constrained projects
- Need to change behavior frequently
When to Fine-Tune
- Specialized domain (medical, legal, technical)
- Consistent style or format required
- Context window is a bottleneck
- Thousands of training examples available
- Performance is worth the investment
The Hybrid Approach
Use fine-tuning for style + prompting for context:
- Fine-tune on your writing style
- Use RAG for knowledge retrieval
- Prompt for specific task instructions
Cost Comparison
| Method | Setup Cost | Per-Use Cost |
|---|---|---|
| Prompting | $0 | Token costs |
| Fine-tuning | $100-10,000+ | Lower token costs |