The Golden Rule: Form vs. Fact
The Confusion
Beginners often say: \"I want to train an AI on my company's PDFs so it knows our return policy.\"This is wrong. That is a use case for RAG (Retrieval Augmented Generation).
The Distinction
- RAG is for Knowledge (Facts): If the answer is in a document (prices, policies, history), use RAG. RAG is like giving the AI an open textbook during the exam.
- Fine-Tuning is for Behavior (Style): If you want the AI to speak in a specific format (JSON), a specific tone (pirate, lawyer, helpful assistant), or follow a complex reasoning pattern, use Fine-Tuning. Training is like sending the AI to medical school; it changes how it thinks, not just what it can read.
Real-Life Example
If you train a model on a specific 2024 Price List, and then you change your prices in 2025, you have to re-train the entire model (expensive). If you use RAG, you just swap the PDF file (free).
Summary Table
| Goal | Solution | Cost |
|---|---|---|
| Know specific facts | RAG | Low |
| Cite sources accurately | RAG | Low |
| Speak in a unique brand voice | Fine-Tuning | High |
| Output consistently in JSON | Fine-Tuning | High |
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Curriculum: 8.9.9.1 - Concepts: Fine-Tuning vs. RAG (Difficulty: Hero | Path: Lab)
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