SFT (Supervised Fine-Tuning)
What is Fine-Tuning?
Supervised Fine-Tuning (SFT) is the process of training an existing pre-trained model on your specific data to adapt it to your domain or task. It adjusts the model's parameters to specialize in your use case.
The Core Idea: Take a general-purpose LLM → Train it on your data → Get a specialized model
Why Fine-Tuning:
- Improve performance on domain-specific tasks
- Learn specialized terminology and patterns
- Reduce prompt engineering needs
- Better consistency in outputs
Frontend Engineer Recommendation
For Implementation: Hire ML Engineers
As a frontend developer, you generally should not be implementing SFT yourself unless using a managed service like OpenAI's fine-tuning API. This is typically the domain of Machine Learning Engineers.
SFT vs RAG vs Prompt Engineering
| Aspect | Prompt Engineering | RAG | Fine-Tuning (SFT) |
|---|---|---|---|
| Cost | Very Low (free) | Low (inference + retrieval) | Very High (GPU training) |
| Setup Time | Minutes | Hours to days | Days to weeks |
| Data Needed | None (just prompts) | Documents for retrieval | 100s-1000s labeled examples |
| Speed | Fast | Medium (retrieval overhead) | Fast (after training) |
| Updates | Instant | Instant (update knowledge base) | Requires retraining |
| Best For | General tasks | Dynamic knowledge | Specialized domains |
When to Use Fine-Tuning
✅ Good Use Cases
Specialized Domain Language
- Medical diagnosis (medical terminology)
- Legal document analysis (legal jargon)
Consistent Output Format
- Always need specific JSON structure
- Code generation in proprietary framework
Style and Tone
- Brand-specific writing style
- consistent personality across responses
Performance Optimization
- Need smaller model with specialized capability to reduce costs/latency
❌ Bad Use Cases (Use RAG Instead)
Frequently Changing Information
- Product catalogs
- News and updates
Large Knowledge Bases
- Company wikis
- Technical manuals
Limited Budget
- Startups without GPU access
- Prototype/MVP stage
Services for Fine-Tuning
If you decide SFT is necessary, these platforms offer managed fine-tuning services that don't require managing GPU infrastructure:
- OpenAI Fine-Tuning (GPT-3.5, GPT-4)
- Anthropic Fine-Tuning (Claude Haiku)
- Azure OpenAI Service
- Together AI (Open source models)
- Anyscale (Open source models)
Next Steps
- Evaluate if you really need SFT (try RAG first)
- Start with OpenAI's fine-tuning API if you must proceed
- Collaborate with ML Engineers for complex model training