RAG vs Fine-Tuning: When to Use What
Two of the most confusing AI terms explained. Which one does your business actually need?
AINow Take
Most Australian businesses should start with RAG. It's cheaper, faster, and easier to update. Fine-tuning is usually overkill unless you need the model to learn a specific skill or style.
If you’ve looked into customising AI for your business, you’ve hit two terms: RAG and fine-tuning. They sound technical, but the concept is simple. Here’s what you need to know.
RAG (Retrieval-Augmented Generation)
Think of it as: Giving the AI a reference library.
RAG works by retrieving relevant information from your documents and injecting it into the AI’s context when answering. The AI model itself doesn’t change — it just gets better information to work with.
Best for:
- Customer support (pull from your knowledge base)
- Document analysis (search contracts, policies, manuals)
- Any situation where information changes frequently
- When you have lots of existing documents
Cost: Low. You pay for storage (vector database) and retrieval.
Australian business example: A law firm using RAG to let lawyers query years of case documents and get answers with citations.
Fine-Tuning
Think of it as: Training the AI to specialise.
Fine-tuning takes a base model and trains it further on your specific data. The model’s weights actually change. It learns to respond in a specific style or format.
Best for:
- Learning a specific writing style or tone
- Specialised terminology (medical, legal, technical)
- Consistent output formatting
- When you need the model to understand nuanced domain knowledge
Cost: Higher. Training costs + hosting the custom model.
Australian business example: An accounting firm fine-tuning a model to understand Australian tax law terminology and generate BAS-compliant reports.
Comparison
| Factor | RAG | Fine-Tuning |
|---|---|---|
| Setup time | Days | Weeks |
| Cost | Low | Medium-High |
| Easy to update | Yes | No (retrain required) |
| Works with new info | Immediately | Requires retraining |
| Need technical skill | Low-Medium | Medium-High |
| Best for | Information retrieval | Style/behaviour change |
The Verdict
Start with RAG. It’s cheaper, faster, and solves most business problems. Only consider fine-tuning if you’ve tried RAG and found it lacking, or if you specifically need the model to behave differently (not just know different things).
Many businesses end up using both: RAG for current information, fine-tuning for specialised behaviour.