RAG vs fine-tuning: which one does your AI product need?
By Indian Software Developers. Published , 5 min read.
Founders often ask whether they should fine-tune a model on their data. Usually the better first question is: what do you want the model to change — what it knows, or how it behaves?
Use RAG when the model needs to know things
Retrieval-augmented generation looks up relevant documents at question time and gives them to the model as context. The model's knowledge stays current because you update the documents, not the model.
- Your knowledge changes often
- Answers need citations
- Different users may see different documents
- You need to remove information reliably
Use fine-tuning when the model needs to behave differently
Fine-tuning adjusts the model's weights with examples. It is good at teaching a format, a tone, or a narrow task, and at making a small model perform like a larger one on that task.
It is a poor way to add facts: the model can still blend them incorrectly, and there is no source to show the user.
Often, the answer is both
A common production pattern is a fine-tuned small model that follows your output format exactly, fed by a retrieval layer that supplies the facts. Start with retrieval and good prompts, measure with real questions, and fine-tune only when you can name the behaviour you need to change.