The most common misunderstanding
Training a language model on your own data yourself is expensive, slow, and inflexible – and usually unnecessary for "the AI should know our knowledge." RAG (Retrieval-Augmented Generation) takes the opposite approach: instead of laboriously teaching a model what's in your manuals, contracts, or tickets, the system searches those exact sources for every request and hands the matching excerpts to an existing language model.
How RAG works – in five steps
A question comes in, asked in plain language. The system then specifically searches your own company data for matching passages, pulls out only the few genuinely relevant excerpts, and passes them to an existing language model together with the question. That model composes a clear answer from them – grounded in your data, not its general training knowledge – and ideally names the source. The model itself is never changed or trained in the process.
RAG or fine-tuning?
Fine-tuning (retraining a model with your own examples) only pays off in a few, very specific cases – say, when a particular style, a fixed format, or specialized jargon needs to be hit reliably. For "the AI should know and use our knowledge," RAG is almost always the better choice: cheaper, faster to update (add a new document, done), and lower-maintenance.
Where the real work is
The most common misconception is that the difficulty lies in the language model. In reality, the real work happens beforehand: preparing and structuring your data sources so the search in step two actually finds the right passages. A RAG system is only ever as good as the data it can search – this part is underestimated in most explanations.
Why this matters for you as a decision-maker
RAG has three concrete advantages: answers are traceable because they point to a real source (which lowers the hallucination risk from module 4); data sovereignty can be controlled granularly – depending on the setup, your data can stay entirely in-house, and the model only ever receives what's needed for the specific question; and it's significantly cheaper than a custom model. If a vendor suggests "we'll train a model on your data," it's worth asking whether RAG wouldn't do the job instead.