RAG vs Fine-tuning: Which One Is Worth It?

To make AI know your business, should you use RAG or fine-tuning? Compare how they work, strengths, costs, and a practical decision framework.

"Should we train AI on our own company data?" is a question many executives ask after trying ChatGPT or Claude. "Train" sounds like the right answer, but in many cases what an organisation actually needs is not a newly trained model, but for the AI to access the right information when it is needed. This article helps you tell clearly when to use RAG and when to use fine-tuning.

What are RAG and fine-tuning?

RAG: have the AI open the documents before answering

RAG (Retrieval-Augmented Generation) does not change the model at all. It searches for relevant documents and attaches them to every question. Think of a capable employee who checks the company manual before answering.

Fine-tuning: train the model further

Fine-tuning takes an already-trained model and trains it further on your examples to adjust its behaviour, such as writing style, answer format, or skill at a specific task. Think of sending an employee to training until a certain way of working becomes second nature.

Today, techniques cheaper than training the whole model are popular, such as LoRA from research by Hu et al. (2021). It freezes the model's original weights and trains only small added matrices. The paper reports that, compared with fully fine-tuning GPT-3 175B, LoRA cut the number of trainable parameters by about 10,000 times and GPU memory needs by about 3 times.

What the research says

The paper Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs (Ovadia et al., published at EMNLP 2024) compared two ways of "injecting knowledge" into a model. Unsupervised fine-tuning helped somewhat, but RAG consistently performed better, both for knowledge the model had seen before and for entirely new knowledge. It also found that models struggle to learn new facts through this kind of fine-tuning.

The practical conclusion: if the goal is "make the AI know information", start with RAG. Fine-tuning is better suited to "make the AI work in a particular way".

Comparing them dimension by dimension

• Frequently changing information: with RAG you just update documents; fine-tuning needs retraining.
• Need to cite sources: RAG can point to the document an answer came from; fine-tuning makes this hard.
• Controlling style and format: fine-tuning does this well, for example answering in your brand tone or producing the same structure every time.
• High-volume repetitive tasks: such as classifying tickets or extracting data from the same form. Fine-tuning a small model may be cheaper in the long run.
• Team skills: RAG is easier to start. Fine-tuning needs a good-quality example dataset and people who know how to evaluate results.

An example from a Thai business

An insurance company wants an AI assistant for its customer service team. The job has two parts. The first is answering the conditions of each policy correctly, which change with each product version; this uses RAG pulling from the latest policy documents. The second is drafting customer reply letters in the company's polite tone with a standard structure. If a prompt with examples still isn't consistent enough, they then consider fine-tuning for this part. The result is using both together, each doing what it does best.

Watch out for

• Fine-tuning on data that contains personal information can lead the model to memorise and reveal it. Always screen data before training.
• A fine-tuned model needs ongoing care when the base model gets a new version, a hidden cost people often forget.
• RAG has costs too: maintaining the document library, the search system and evaluation. It is not a maintenance-free shortcut.
• Before doing either, improve your prompts first. Many problems can be solved at this stage.

RAG changes what AI knows. Fine-tuning changes how AI works.

A 5-step decision framework

1. Define the problem clearly: is it "the AI doesn't know the information" or "the AI doesn't work the way we need"?
2. Start with a good prompt plus examples, and measure with a set of real questions.
3. If the problem is information, build RAG and tune the search well.
4. If the problem is format or behaviour that prompts can't control, then consider fine-tuning with a quality example set.
5. Measure before and after every time to make sure the extra investment is really worth it.

If you are not yet familiar with how RAG works, start with What is RAG. And to practise prompt writing, the first step of every project, see the course Write prompts that get the results you want on Plants.work.