why use RAG

A base LLM only knows what was in its training data, frozen at some past date, and it has never seen your company's private wiki or last week's news. RAG addresses both gaps at once. Point the model at your own documents and it can answer from them with no retraining; refresh those documents and the system instantly knows the new facts. For private or fast-moving knowledge, this is usually far cheaper and simpler than baking the information into the model's weights.

The second payoff is trust. Because each answer is assembled from specific retrieved passages, you can show the user exactly where every claim came from, and a model reading real evidence is much less prone to inventing things. RAG is not magic — retrieval can miss, and the model can still misread what it found — but as a way to ground answers in current, checkable sources, it has become the default first tool for connecting an LLM to real-world knowledge.