Retrieval-Augmented Generation decouples an AI's reasoning engine from its static memory.

Traditional AI models rely strictly on static training data. When their knowledge cutoff date passes, they become blind to new events. Retrieval-Augmented Generation, or RAG, solves this. Instead of guessing from memory, the system acts as a digital scout. It queries live databases or search engine APIs first. It then grabs relevant text snippets and feeds them directly to the model. This allows the AI to write a factual, up-to-date response without needing expensive retraining.