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.

Natural Language Understanding (NLU), a core technology within Natural Language Processing (NLP), is an AI capability that accurately interprets context and intent from text and speech. Unlike simple word processing, NLU serves as the foundation for conversational AI, powering applications such as chatbots, voice assistants, and sentiment analysis. In business, NLU contributes to productivity gains and cost reduction by automating customer interactions and streamlining data analysis.

Natural Language Generation (NLG) is a technology that automatically produces human-readable text or speech from structured and unstructured data. While its counterpart, Natural Language Understanding (NLU), focuses on comprehension, NLG transforms data into meaningful narratives. It is widely applied in chatbots, voice assistants, automated reports, and other business contexts.

Natural Language Understanding (NLU) is a critical area of Natural Language Processing (NLP) that enables computers to accurately interpret human language in context. Beyond recognizing individual words, NLU can grasp user intent and emotions, making it applicable to chatbots, voice assistants, sentiment analysis, and more. This enhances conversational AI and data-driven decision-making.