Natural Language Generation (NLG) differs from Natural Language Processing (NLP) in their core functionalities. NLG focuses on producing human-like text from structured data, effectively converting information into readable formats, such as generating reports or narratives. In contrast, NLP involves the understanding and interpretation of human language, enabling systems to process and analyze text or speech. While NLG is primarily concerned with content generation, NLP encompasses a broader range of tasks, including sentiment analysis, translation, and information extraction, making both vital for advancements in AI and machine learning applications.