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When to Fine-Tune
The Customization Decision Framework
Fine-tuning isn't always the right answer. Use this framework to decide:
| Approach | When to Use | Cost | Effort |
|---|---|---|---|
| Prompt Engineering | Model can do the task with better instructions | Free | Low |
| Few-Shot Examples | Model needs examples of desired output format | More tokens | Low |
| RAG | Model needs access to specific knowledge | Search costs | Medium |
| Fine-Tuning | Model needs to learn new behavior/style/format | Training + hosting | High |
| Distillation | Need a smaller model that mimics a larger one | Training | High |
Fine-Tuning Is Right When:
- You need consistent output format/style that prompting can't achieve
- You're processing domain-specific jargon the base model doesn't understand
- You want to reduce latency/cost by using a smaller fine-tuned model
- You need the model to follow complex business rules reliably
🚧 Golden Rule: Always try prompt engineering and RAG first. Only fine-tune when those approaches demonstrably fail. Fine-tuning is expensive and creates maintenance burden.
⌨ HANDS-ON LABEvaluate Fine-Tuning Feasibility Matrix
⭐ +150 XPRun validation script comparing RAG vs Fine-Tuning metrics for specific task datasets.
1Execute Python evaluation comparing token costs and latency across RAG and fine-tuning.
OBJECTIVE 1 / 1 — type "hint" if stuck
FOUNDRY VERIFICATION
QUERY 1 // 1
What should you always try before fine-tuning a model?
Nothing - fine-tune immediately
Prompt engineering and RAG first
Build a custom model from scratch
Switch to a different cloud provider