A plain-language comparison of two ways to give an AI agent knowledge about your business — and why most businesses should start with one of them.
To be useful, an AI agent has to know things about your business — your products, prices, policies, and hours. There are two main ways to give it that knowledge, and the difference matters for cost, accuracy, and how often you can update it.
Fine-tuning means retraining the AI model itself on your information. It bakes the knowledge in. The downsides: it is slow and expensive to do, you have to redo it every time a price or policy changes, and the model can still confidently invent details it half-remembers.
RAG — retrieval-augmented generation — works differently. Your information stays in a searchable knowledge base. When a customer asks a question, the system looks up the most relevant facts and hands them to the AI along with the question. The AI answers using those facts, like an employee reading from an up-to-date handbook.
For almost every business, RAG is the better starting point. You can update a price or add a new FAQ and it takes effect within seconds — no retraining. Answers are grounded in real documents, so the agent is far less likely to make things up. And because the source is visible, you can see exactly why the agent said what it said.
Fine-tuning still has its place — for example, teaching a model a very specific tone of voice or a specialised format. But for keeping an agent accurate about facts that change, RAG wins. That is why Partython's Brain is built on RAG: you upload your documents, FAQs, and catalog, and your agent is an expert within minutes.