When building an LLM application, there is usually a point where prompt engineering is no longer enough. You may need the model to know information it has never seen before. Or you may want it to behave differently for a specific task. This is where two common approaches come in, RAG and Fine-Tuning. They are often discussed together, but they solve different problems.
The Core Difference
RAG changes what information the model can access. And Fine-tuning changes how the model behaves.
Imagine you are building an internal company assistant. Employees may ask what our current reimbursement policy is. The base LLM probably does not know your company's latest internal policy. You could use RAG to retrieve the relevant policy document and provide that information to the model before it generates the answer.
The model itself has not learned the policy. It simply receives the right information when it needs it. This makes RAG especially useful when the problem is missing knowledge.
Fine-Tuning Solves a Different Problem
Now imagine the model already has the information it needs, but you want it to perform a specific task in a consistent way. This is where fine-tuning can help. For example, we are building a medical AI system that needs to classify clinical notes into specific categories. We may already have thousands of examples written and labeled by medical experts. We can use these examples to fine-tune the model, helping it learn the patterns needed for this specific task. After training on many examples, the model becomes better at performing this specific type of classification. So while RAG gives the model information it does not have, fine-tuning helps the model learn how to perform a task.
A Simple Rule
When deciding between them, the first thing to ask is what is actually missing. If the model is missing knowledge, RAG may be the better solution. If the model needs to learn a specific behavior or task pattern, fine-tuning may be more appropriate. And in many real systems, we do not have to choose only one. We can use both. In the next post, we'll look at how RAG and fine-tuning can work together in a real LLM application.
