Choosing the Right Customization Strategy

One of the most common questions in AI development: should you fine-tune a model or just improve your prompts? The answer depends on your specific situation.

When Prompting Is Enough

Stick with prompting when:

• Task is well-defined: Clear instructions get good results

• Quality data is limited: Fine-tuning needs substantial examples

• Requirements change frequently: Prompts are easy to update

• Budget is constrained: Prompting costs only inference

When Fine-Tuning Shines

Consider fine-tuning for:

• Specific style or tone: Matching brand voice consistently

• Domain expertise: Medical, legal, technical terminology

• Format consistency: Structured outputs that must be exact

• Latency requirements: Shorter prompts mean faster responses

"We tried prompting for months. Fine-tuning on 500 examples got us better results in a week." — ML engineer at startup

Hybrid Approaches

Many production systems combine both: fine-tuned base models with task-specific prompting. This balances customization with flexibility.

Cost-Benefit Analysis

Fine-tuning has upfront costs but reduces per-request costs through shorter prompts. Calculate break-even based on expected query volume.