new knowledge into the model or for situations that demand
quick iteration for new use cases.

Fine-tuning is similar to having students internalize knowl-
edge through prolonged learning. This method is applicable
when the model needs to replicate specific structures, styles,
or formats. Fine-tuning can achieve performance superior to
non-fine-tuned models, and interactions are more efficient.
Fine-tuning is particularly suitable for emphasizing existing
knowledge in the base model, modifying or customizing the
model’s output, and instructing the model with complex di-
rectives. However, fine-tuning is not suitable for adding new
knowledge to the model or for scenarios that require rapid it-
eration for new use cases. The specific comparison between
RAG and Fine-tuning (FT) can be elucidated in Table 1.
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey