• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.159-172
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• 2020

Social networking services (SNS) are developing significantly with the Internet and smartphones. It's a friendly social media, but if you think deeply about it, you'll find that it has a variety of faces. It is a communication tool between users, a medium for delivering information, an infrastructure for providing applications, and a community where people with common interests gather. In recent years, business tools, shopping and payment methods are also being swallowed. The influence of the spread of SNS on the real world is also expanding, and the work being dealt with from a sociological perspective is also increasing. Also, if you pay attention to the technical aspects of SNS, it is composed of various technical elements, such as infrastructure that handles large-scale access, user interface that supports comfortable use, and big data analysis to understand people's behavior more deeply. However, I usually use it as usual. However, if you look through SNS, you can see that the situation is surprisingly profound and multifaceted. This study began by looking at the history and current status of SNS and attempted to find its status through comparison with other media. From the point of view of relationship with society, it can be a risk and legal issue when using SNS, such as crimes using bad social media or social media. It is also necessary to comment on the activities on SNS or the guidelines established by the operators. Therefore, various legal issues on SNS will be discussed. Also, as an example of using SNS, I will introduce an example of using SNS in disaster response. From a more technical point of view, you will receive commentary on SNS's network-based technology and SNS's information use, and these articles will help you understand and use SNS safely and help you further utilize or develop SNS.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.129-164
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• 2023

In a situation where the use and introduction of Large Language Models (LLMs) is expanding due to recent developments in generative AI technology, it is difficult to find actual application cases or implementation methods for the use of internal company data in existing studies. Accordingly, this study presents a method of implementing generative AI services using the LLM application architecture using the most widely used LangChain framework. To this end, we reviewed various ways to overcome the problem of lack of information, focusing on the use of LLM, and presented specific solutions. To this end, we analyze methods of fine-tuning or direct use of document information and look in detail at the main steps of information storage and retrieval methods using the retrieval augmented generation (RAG) model to solve these problems. In particular, similar context recommendation and Question-Answering (QA) systems were utilized as a method to store and search information in a vector store using the RAG model. In addition, the specific operation method, major implementation steps and cases, including implementation source and user interface were presented to enhance understanding of generative AI technology. This has meaning and value in enabling LLM to be actively utilized in implementing services within companies.