Fine-Tuning and Customization
Fine-tuning means additional training on your specific data, adjusting the model's weights to change its behavior or knowledge. It sounds powerful — and it is — but it's often overkill. Most applications achieve their goals with simpler approaches.
What Fine-Tuning Actually Does
During fine-tuning, you provide examples of desired inputs and outputs. The model trains on these examples, adjusting its internal parameters to better match your patterns. The result is a customized model that behaves differently from the base model.
Fine-tuning process:
1. Prepare training data (prompt/completion pairs)
2. Upload to provider or run locally
3. Train (hours of compute time)
4. Deploy your customized model
5. Use like any other model
This process costs money, takes time, and requires maintaining a separate model version.
When Fine-Tuning Makes Sense
Consider fine-tuning when:
- You need a very specific output format consistently across thousands of requests
- Your domain uses specialized terminology the base model handles poorly
- You require a particular tone or style that prompting can't reliably achieve
- You have high volume with a consistent task type
These situations are rarer than you might think.
Alternatives That Usually Work Better
Prompt engineering solves most problems. Better prompts, clearer instructions, and few-shot examples (showing the model what you want) often achieve the goal without any training.
Instead of fine-tuning for JSON output:
"Return your response as valid JSON with this structure:
{
"summary": "...",
"key_points": ["...", "..."],
"sentiment": "positive|negative|neutral"
}
Example:
Input: [example input]
Output: [example JSON output]"
RAG handles knowledge gaps. If the model needs to know about your products, documentation, or data — retrieve that information at query time rather than training it into the model. RAG is easier to update and doesn't require retraining when information changes.
System prompts establish consistent behavior across conversations. Define the model's role, constraints, and output expectations once, and they apply to every interaction.
Decision Framework
Need specific knowledge? → Try RAG first
Need specific format/style? → Try prompt engineering first
High volume, consistent task, still not working? → Consider fine-tuning
Start simple. Add complexity only when simpler approaches demonstrably fail. Fine-tuning is a powerful tool, but reaching for it too early creates unnecessary maintenance burden and cost.