LLM optimization has received significant attention due to
its increasing prevalence. Techniques such as prompt engi-
neering, Fine-Tuning (FT), and RAG each have distinct char-
acteristics, visually represented in Figure 6. While prompt
engineering leverages a model’s inherent capabilities, opti-
mizing LLMs often requires the application of both RAG and
FT methods. The choice between RAG and FT should be
based on the specific requirements of the scenario and the in-
herent properties of each approach. A detailed comparison of
RAG and FT is presented in Table 1.
RAG forLargeLanguageModels-ASurvey