With advances in large language models (LLMs), the scope of generative AI use in enterprises and research institutions has expanded more than ever. However, simply using an LLM as-is is often insufficient to meet real-world business requirements. For example, when you want to incorporate the latest information or need responses grounded in internal knowledge, an LLM alone cannot handle it. In addition, for long-text processing and complex context understanding, gaps in knowledge and hallucinations (hallucination) become issues.

With the advancement of DX, generative AI is now widely used in text creation, customer support, and data analysis, contributing to improved efficiency and competitiveness. However, at the same time, a serious issue known as “hallucination” has emerged—when AI generates information that appears plausible but is in fact incorrect.