Choosing the right architecture for enterprise data: fine-tuning or RAG?

When you bring closed, proprietary in-house data together with large language models, the most critical architectural decision is how that data reaches the model. The first answer that comes to mind is usually to train or retrain the model on the organisation's data — fine-tuning. But trying to solve every problem that way brings an enormous compute cost and a serious loss of time. On top of that, updating or deleting the data inside a fine-tuned model is architecturally very hard.

This is where RAG — Retrieval-Augmented Generation — offers enterprises a far more flexible and dependable alternative. RAG gives the model the ability to work from current data, and at the same time largely prevents it from hallucinating. Instead of locking knowledge into the model's parameters, it pulls from the corporate database at the moment of need, which optimises cost and keeps accuracy high.