Fine-Tuning vs RAG
Learn when to use fine-tuning, RAG, prompting, or a combination for AI Engineering problems.
8 min
RAG and fine-tuning solve different problems. RAG gives the model fresh external context. Fine-tuning changes model behavior based on training examples.
Use RAG When
- Knowledge changes often.
- The answer should cite source material.
- Data is private, tenant-specific, or access-controlled.
- You need to update content without retraining.
- The model needs a small slice of a large knowledge base.
Use Fine-Tuning When
- You have many high-quality examples of the desired behavior.
- The output style or task pattern is stable.
- Prompting is too brittle or too verbose.
- You can evaluate improvements against a baseline.
Compare Options
| Need | Better First Tool |
|---|---|
| Fresh policy docs | RAG |
| Specific writing style | Fine-tuning |
| Private customer knowledge | RAG with permissions |
| Repeated extraction format | Prompting, then fine-tuning if needed |
| Lower hallucination risk | RAG plus evaluation |
Combine Carefully
Many mature systems use both. For example, a fine-tuned model may follow a support-answer format while RAG supplies current product documentation.
Evaluation First
Before fine-tuning, create a baseline and an evaluation set. Otherwise it is hard to know whether the trained model improved the product or simply changed behavior.
Next Step
Take the fine-tuning quiz, then choose one use case and explain whether RAG, prompting, fine-tuning, or a combination is the right first move.
Practice this topic
Reinforce the concepts from this lesson with a short quiz and explanation review.
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