How does Retrieval-Augmented Generation (RAG) overcome the static nature of a model's weights to maintain accurate performance on data that changes after the model has finished training?

Large Language Models possess static weights, meaning the internal information they learn during training is frozen and cannot be updated once the process concludes. To address this, Retrieval-Augmented Generation decouples the model's knowledge from its reasoning capabilities by introducing an external, dynamic database. When a user submits a query, the RAG system first performs a search across a collection ....