Why Fine-Tune?

When to Fine-Tune LLMs

Fine-Tuning vs Alternatives

| Approach | Best For | |----------|----------| | Prompt Engineering | Quick tasks, flexibility | | RAG | Changing knowledge, citations | | Fine-Tuning | Specific formats, domain adaptation |

When to Fine-Tune

✅ Consistent output formats needed ✅ Domain-specific language/vocabulary ✅ Style and tone customization ✅ Efficiency at scale ✅ 1,000+ high-quality examples available

When NOT to Fine-Tune

❌ Limited data (<100 examples) ❌ Task changes frequently ❌ Need reasoning about new information ❌ Prompt engineering works fine

The LoRA Revolution

Traditional fine-tuning: Update all parameters (100+ GB VRAM) LoRA: Update small adapter layers (6-16 GB VRAM)

Full fine-tuning: 70B params = 1.1 TB memory
LoRA (r=16): 4M params = 16 GB memory

→ Proceed to Lesson 2: LoRA & QLoRA