Retrieval-Augmented Generation has become the default recommendation for almost every enterprise LLM project, to the point where fine-tuning is treated as exotic or unnecessary. That’s an overcorrection. Both approaches solve real problems; they solve different ones.
What each approach actually solves
RAG solves the knowledge freshness problem. The model doesn’t need to know facts — it retrieves them at query time from a store you control. It’s the right tool when the information changes frequently, when you need source attribution, or when the knowledge base is too large to fit in a context window.
Fine-tuning solves the behaviour and style problem. You can’t RAG your way to a model that consistently responds in a specific tone, formats outputs a specific way, or handles a domain-specific task type reliably.
The decision matrix
| Need | Approach |
|---|---|
| Access to up-to-date information | RAG |
| Consistent output format/structure | Fine-tuning |
| Domain-specific terminology and tone | Fine-tuning |
| Attribution and source transparency | RAG |
| Reducing hallucination on facts | RAG |
| Few-shot task specialisation | Fine-tuning |
What we tell clients who want to start with fine-tuning
Build the RAG pipeline first. It’s faster, cheaper, and easier to iterate. Fine-tune only after you’ve identified a specific, persistent failure mode that retrieval can’t fix. Fine-tuning on top of a good RAG baseline almost always outperforms fine-tuning alone.