Fine-tuning a language model allows for a customized understanding of specific data, which can enhance performance on narrow tasks. However, this can lead to overfitting or reduced generalization. In contrast, RAG combines pretrained models with an external knowledge base, providing real-time access to vast information while maintaining generalization, but it can introduce latency during retrieval.