## 2 Related Work
### Research on RAG  
The typical process of an RAG and the differences between implementing LLM with and without RAG are shown in Figure 1[13; 14; 15]. Consider a scenario wherein a user asks ChatGPT about the number of foreign students at a university. Because ChatGPT relies on pretrained data, it initially lacks the ability to provide the most recent data. As shown on the left side of Figure 1, it cannot provide the exact number and only outputs abstract information. By contrast, RAG allows it to retrieve knowledge from external databases; therefore, it can refer to the university database and provide the exact number. Thus, RAG improves the search performance of ChatGPT, similar to providing it with a personalized textbook for information retrieval.  
[PERSON] et al. [15] analyzed the various components of RAG. Recently, modular RAG techniques that increase the flexibility of RAG by incorporating new modules and other techniques such as fine-tuning have emerged. For example, the implementation and advantages and disadvantages of naive, advanced, and modular RAG models were analyzed for RAG applications.  
[PERSON] et al. [16] introduced practical RAG applications and benchmarks by categorizing the fundamental processes of RAG and investigating various search and generator augmentation methodologies. They investigated the impact of search augmentation on LLMs and analyzed the RAG performance in terms of noise, negative rejection, information integration, and semantic robustness. They experimented with four separate testbeds basedon the aforementioned basic capabilities for evaluating RAG in English and Chinese. The results showed that LLMs exhibit a certain level of noise robustness but still suffer from significant difficulties in negative rejection, information integration, and handling false information. These results suggest the need for designing applications more deeply and using databases to enhance the benefits of RAG. In this study, we implemented an application system that can fully leverage RAG by building and retrieving personalized information.  
[PERSON] et al. [17] integrated domain refinement and RAG to implement a QA method for providing accurate information on typhoon disasters. They trained the T5 LLM on typhoon disaster information from open-source databases, such as Baidu Encyclopedia and Wikipedia. The RAG module was employed to enhance answers to user prompts by retrieving semantically similar phrases from external knowledge bases. They evaluated the typhoon agent (Typhoon-T5) using a similarity matching approach and laid the foundation for integrating LLMs and disaster information. Thus, employing RAG in conjunction with LLMs can effectively improve the search performance for a specific topic.  
[PERSON] et al. [18] reviewed search-augmented text generation and other notable approaches for various text-generation tasks, including dialog-response generation and machine translation. They summarized the various components of search-augmented text generation, including search metrics, search sources, and integration paradigms, and provided useful information for developing application-specific topics during RAG.  
Thus, the efficacy of compensating for the shortcomings of LLMs, such as hallucinations, and improving search efficiency is being actively investigated. This study demonstrates that RAG can be effectively implemented to build a personalized database that provides differentiated search and QA services, along with improved search accuracy.