What each one does
- RAG looks up relevant passages from your own sources at the moment a question is asked and gives them to the model, so the answer can cite where it came from. The approach was introduced in Lewis et al., 2020 (https://arxiv.org/abs/2005.11401).
- Fine-tuning trains a model further on your examples so it changes how it responds. OpenAI's guide describes it as a way to get more consistent results for a specific task, and recommends trying prompting first (https://platform.openai.com/docs/guides/fine-tuning).
What the research says
- A Microsoft study comparing the two for adding knowledge found that RAG consistently outperformed unsupervised fine-tuning, for both knowledge the model had seen and entirely new knowledge (Ovadia et al., https://arxiv.org/abs/2312.05934).
- Another Microsoft case study found the two can be combined, with gains from fine-tuning adding to gains from RAG (Balaguer et al., https://arxiv.org/abs/2401.08406).
These are single studies on specific data, so test on your own examples before you decide.
A simple way to choose
- Is the problem missing or changing facts? Start with RAG.
- Is the problem format, tone or a narrow repeated task? Try better prompts and examples first; fine-tune if they don't hold.
- Do you need answers people can check? RAG, because each answer can link to its source.
- Both? Use RAG for the facts and a fine-tuned model for the behavior, but only after the simpler setup is measured.
Where we come in
We help teams pick the simplest setup that works, test it on their real questions, and build it into their tools. See our AI knowledge search scenario and document processing, or start with AI consulting.
FAQ
What is the difference between RAG and fine-tuning?
RAG gives the model your information at the moment it answers. Fine-tuning retrains the model on examples so it behaves differently every time.
Is RAG cheaper than fine-tuning?
Often it's simpler to start, since there's no training run and you update facts by updating documents. Actual cost depends on your volume and setup.
Can fine-tuning teach a model new facts?
It can, but research comparing the two found retrieval did better at adding knowledge, and retrained facts go stale when your data changes.
Can I use RAG and fine-tuning together?
Yes. A common pattern is RAG for current facts and a fine-tuned model for format or tone. Start with one and measure before adding the other.
Does RAG stop hallucinations?
It reduces them by grounding answers in your sources, but it doesn't remove them. Show sources so people can check.
Who is CTO.LA?
CTO.LA is a technology and AI consulting firm in Beverly Hills, California, founded in 2025. Reach us at hello@cto.la.