RAG vs fine-tuning: which does your business actually need?

When businesses want an AI assistant that knows their policies, products or past cases, two approaches come up: retrieval-augmented generation (RAG) and fine-tuning. They are often confused, but they solve different problems.

RAG: look it up, then answer

With RAG, your documents are split into passages and indexed. When someone asks a question, the system finds the most relevant passages and gives them to the language model with the question. The model answers using those passages and can show where each part of the answer came from.

RAG is usually the right choice when:

• Your information changes often, such as prices, policies or product details.
• Answers must be traceable to a source document.
• You need to control who can see which documents.
• You want to start quickly and measure accuracy on real questions.

Fine-tuning: change how the model behaves

Fine-tuning trains a model further on your examples so it learns a style, a format or a narrow task, such as classifying support tickets into your categories or writing in your brand's tone. It does not reliably teach the model new facts, and updating it means training again.

Fine-tuning can make sense when:

• The task is narrow and repetitive, with many good examples.
• You need a consistent output format or tone.
• You need a smaller, cheaper model to perform one job well.

A simple way to decide

Ask what is missing. If the model lacks knowledge of your business, use RAG. If it lacks behaviour (it knows enough but responds in the wrong way or format), consider fine-tuning. Many good systems use RAG for knowledge and careful instructions for behaviour, and never need fine-tuning at all.

What makes either approach work

Whichever you choose, the deciding factor is evaluation. Collect real questions with correct answers before building, and measure the assistant against them. Add guardrails so it says when it doesn't know, log every answer, and give users an easy way to reach a person.

Costs to plan for

RAG costs include document preparation, an index (vector database) and model usage per question. Fine-tuning adds training runs and retraining when things change. We estimate running costs during a proof of concept, so you know them before you scale.

If you're weighing up an AI assistant for your team or customers, book a free consultation and we'll help you choose the simplest approach that works.