A simple explanation of the mechanism and creation of RAG (Retrieval-Augmented Generation) illustrated.
You can understand what a RAG chatbot is in five minutes. It explains the mechanism of RAG (Retrieval-Augmented Generation), the differences from standalone ChatGPT, how to create it using LangChain and vector databases, and the criteria for deciding between in-house development and outsourcing, all illustrated. It includes a real example of reducing internal help desk inquiries by 60%. For more details, please see the technical column in the related links.
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basic information
【Technical Elements】 - RAG (Retrieval-Augmented Generation) - LangChain - Vector DB - Differences from ChatGPT - Criteria for in-house development vs. outsourcing 【Category】AI・Chatbot *For more details, please refer to the technical column in the related links.
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Applications/Examples of results
【Intended Use】 - Consideration of implementing a RAG chatbot - Automation of internal help desk inquiries - Basic understanding of AI utilization 【Examples of Achievements】 - Reduced internal help desk inquiry responses by 60%
Company information
Technosphere Co., Ltd. is a system development company based in Osaka that tackles customer challenges in advanced technology areas such as AI, IoT, and web system development. Since its founding in 2021, the company has leveraged a flat team structure to achieve a flexible and speedy development style. We are engaged in solutions that directly address social issues, such as image inspection AI and smart factory support systems.
