Models
Fine-tuning or RAG? When to use which
baykAI Team
Both bring a model closer to your organisation, but they solve different problems. We separate the parts teams usually confuse.
The question enterprise teams ask most often is this: "Should we train the model on our own data, or have it search our documents?" The two get discussed as alternatives, but they solve different problems.
The distinction fits in one sentence: RAG gives the model knowledge, fine-tuning teaches the model behaviour.
What RAG does
RAG (retrieval-augmented generation) searches your own documents when a question arrives, feeds the matching passages to the model, and has it write the answer from those. The model learns nothing; it only uses the text handed to it in that moment.
It is the right choice when:
- Information changes often (price lists, stock, regulation, product documentation)
- You need to show the source of an answer
- Document volume is large and constantly growing
- You want to start quickly: setup takes days, not weeks
What fine-tuning does
Fine-tuning updates the model's weights with your examples. The model then knows how to speak, what format to answer in, and the terms specific to your field.
It is the right choice when:
- The model must answer in a particular tone or a fixed format
- The language of your field differs from what general models know (law, insurance, medicine, banking)
- You will run the same task millions of times: shorter prompts lower cost
- You have a narrow, repetitive task such as classification or labelling
Side by side
| Question | RAG | Fine-tuning |
|---|---|---|
| How often does the information change? | Constantly | Rarely |
| Do you need to cite sources? | Yes | No |
| Setup time | Days | Weeks |
| Data required | Documents | Labelled examples |
| Updating later | Change the document | Retrain |
For most teams the answer is both
In real projects the setup that works best is usually this: fine-tuning fixes the tone and the format, RAG feeds the current information.
Take an insurance company: fine-tuning teaches the model policy language and the mandatory disclaimers, while RAG retrieves the customer's actual policy details. Neither can do that job alone.
Where to start
Start with RAG almost every time. Setup is fast, results are measurable, and you see your mistakes cheaply. Bring in fine-tuning only when a problem remains that RAG cannot solve. That problem is usually not missing knowledge but inconsistent tone and format.