Given the success of RAG, exploring the model’s adapt-
ability and universality in multi-domain applications will be
part of future work. This includes its use in professional do-
main knowledge question-answering, such as in medicine,
law, and education. In the application of downstream tasks
such as professional domain knowledge question-answering,
RAG might offer lower training costs and better performance
benefits than fine-tuning.

Simultaneously, improving the evaluation system of RAG
for assessing and optimizing its application in different down-
stream tasks is crucial for the model’s efficiency and bene-
fits in specific tasks. This includes developing more accurate
evaluation metrics and frameworks for different downstream
tasks, such as context relevance, content creativity, and harm-
lessness, among others.
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