When a company wants an AI assistant that knows its products, policies or procedures, two approaches come up: retrieval-augmented generation (RAG) and fine-tuning. They solve different problems.
What RAG does
RAG keeps the model as it is and gives it the right information at question time. Your documents are split into passages and indexed for search. When someone asks a question, the most relevant passages are retrieved and passed to the model, which answers using them — and can cite its sources.
What fine-tuning does
Fine-tuning trains the model further on your examples. It is good at teaching a style, a format or a narrow task — for example always replying in a particular tone or classifying tickets into your categories. It is not a reliable way to teach facts that change.
Why most businesses should start with RAG
- Your information changes — update a document and the assistant knows immediately.
- Answers can link to the source, so users can verify them.
- Access control is possible: users only retrieve documents they are allowed to see.
- No training cost or retraining cycle.
When to consider fine-tuning
Consider it once you have a working RAG system and a specific gap: a strict output format, a domain vocabulary the model handles poorly, or a high-volume classification task where a smaller tuned model is cheaper.
Getting the basics right
- Clean, current source documents matter more than the model choice.
- Test with real questions from real users before launch.
- Tell the assistant to say "I don't know" when the answer isn't in the sources.
- Log questions to find gaps in your documentation.
We build RAG assistants, AI agents and chatbots for WhatsApp and the web. See our AI solutions page for examples.