ModelRefs / RAG vs Fine-Tuning: Which One Do You Actually Need?
RAG vs Fine-Tuning: Which One Do You Actually Need?
Both customize an LLM for your use case, but they solve different problems. Here is how to pick the right one — and when to combine them.
When teams want to "make an LLM work for our data", they almost always reach for one of two techniques: retrieval-augmented generation (RAG) or fine-tuning. Picking the wrong one wastes weeks. Here is how to choose.
What RAG does
RAG keeps the base LLM untouched. At query time it:
- Embeds your question into a vector.
- Searches a vector database for the most relevant chunks of your documents.
- Stuffs those chunks into the prompt as context.
- Asks the LLM to answer using that context.
RAG is the right answer when you need the model to know facts it was not trained on — your internal docs, your product catalogue, last week's support tickets.
What fine-tuning does
Fine-tuning takes a pretrained model and continues training it on your own data, adjusting its weights. The result is a new model that has internalised your domain.
Fine-tuning is the right answer when you need the model to change how it behaves — a specific tone of voice, a structured output format, a niche task it consistently fails at, or a specialised domain where general models are weak.
A simple decision guide
| Symptom | Reach for |
|---|---|
| "The model does not know about our products" | RAG |
| "The model keeps citing wrong facts" | RAG |
| "The model will not stick to our JSON schema" | Fine-tuning |
| "The model sounds nothing like our brand" | Fine-tuning |
| "The model is terrible at our niche jargon" | Fine-tuning |
| "Our data changes weekly" | RAG |
When you need both
Production systems often combine the two. Fine-tune the model to follow your output format and tone, then use RAG to feed it fresh facts at query time. This is how most enterprise AI assistants in 2026 are actually built.
Cost reality check
- RAG — cheap to start (vector DB + embedding API), pay-per-query at inference.
- Fine-tuning — upfront training cost, but cheaper per query at scale because prompts are shorter.
Start small
Nine times out of ten, start with RAG. It is faster to build, easier to update, and you will learn what your users actually ask before you commit weights to disk. Only fine-tune once RAG is hitting a ceiling.