
The rapid advancement and enhancement of generative AI technology have presented challenges regarding training and fine-tuning as a sustainable path for widespread adoption. The idea posits that relying on retrieval-augmented generation (RAG) and prompt engineering is currently a more sustainable and efficient strategy than continuous investment in model training and fine-tuning. This strategic approach helps avoid the unsustainable cycle of