Module 11 Lesson 1: When Fine-Tuning Is Needed
RAG vs Fine-Tuning. Knowing when to give the AI a book and when to perform surgery on its brain.
To Tune or Not to Tune?
We just finished Module 10, where we learned that RAG can solve 90% of knowledge-based problems. So why does Fine-Tuning exist? Fine-tuning isn't about giving the AI Facts; it's about teaching the AI Behavior, Style, and specific Domain Logic.
1. RAG is for "What To Know"
Use RAG when you have a 1,000-page manual. The model searches the manual, finds the fact, and tells you.
- Analogy: An open-book test.
2. Fine-Tuning is for "How To Act"
Use Fine-Tuning when you need the model to:
- Speak a new language: (e.g., teaching an English model to be fluent in a rare dialect).
- Master a specific syntax: (e.g., teaching the model to output a custom format that doesn't exist in the wild).
- Control Tone/Style: (e.g., making the model sound exactly like a specific person or brand).
- Master Complex Logic: (e.g., teaching a model to perform medical diagnosis based on symptoms in a way that base models fail).
3. The Comparison
| Feature | RAG | Fine-Tuning |
|---|---|---|
| New Knowledge | Excellent | Hard (requires frequent retraining) |
| Style/Format | Moderate | Excellent |
| Cost | Low (VRAM) | High (GPU compute time) |
| Updates | Instant (add a PDF) | Slow (requires a new training run) |
| Accuracy | High (cites sources) | Lower (can still hallucinate) |
4. The "Hybrid" Approach
In the professional world, the best systems are Both.
- You Fine-tune a model to be an expert in medical terminology and bedside manner.
- You use RAG to give that medical model access to the latest medical journals.
Together, you have a model that "Talks like a doctor" and "Has a doctor's library."
5. Can You Fine-Tune on Your Computer?
A few years ago, the answer was "No." You needed a $10,000 GPU. Today, thanks to PEFT (Parameter-Efficient Fine-Tuning) and LoRA (Lesson 2), you can fine-tune a Llama 3 model on a modern gaming laptop or a Mac Studio in a few hours.
Key Takeaways
- Fine-tuning changes the "Base Behavior" and "Intelligence" of the model.
- RAG is better for "dynamic knowledge" (facts that change).
- Fine-tuning is the choice for style, format, and logic-heavy specialization.
- Most "Local AI" projects should start with RAG and only move to Fine-tuning when RAG hits a wall.