Fine-Tuning vs. RAG
Two ways to customize an LLM for your data — and how to choose.
Inspired by learning material from Microsoft
When you want an LLM to work with your knowledge, you usually choose between fine-tuning and retrieval-augmented generation (RAG).
Fine-tuning
You continue training the model on your own examples so it internalizes a style or task.
- Good for: consistent tone, specialized formats, narrow tasks.
- Downside: costly to update, can't easily add fresh facts.
RAG (Retrieval-Augmented Generation)
You keep the model as-is but retrieve relevant documents at query time and include them in the prompt.
- Good for: up-to-date facts, large knowledge bases, citing sources.
- Downside: depends on retrieval quality and prompt size.
How to choose
- Need current, changing knowledge? RAG.
- Need a specific behavior or style? Fine-tuning.
- Often the best systems combine both.
Key takeaways
- Fine-tuning changes the model's behavior.
- RAG injects fresh knowledge at query time.
- Many real systems use both together.