What is natural language generation (NLG)?

Natural language generation (NLG) is an AI and computational-linguistics field that converts structured data, semantic representations, or other unfinished inputs into readable human language, powering familiar features such as fitness recaps, dashboard explanations, support replies, voice assistants, reports, and personalized messages. It is distinct from natural language processing, the broader discipline of working with language, and natural language understanding, which interprets user intent; together, NLU and NLG enable conversational systems. Traditional NLG involves selecting relevant information, organizing it, combining facts, choosing words and references, and applying grammar, although modern language models learn these processes jointly. Summarization may be extractive, reusing source sentences for reliability, or abstractive, producing new phrasing with greater fluency but a higher risk of errors or hallucinations. NLG techniques range from predictable templates and rule-based systems to statistical models, neural networks, and transformer-based large language models, with each offering different tradeoffs in flexibility and reliability. The text also notes that tools such as Zapier can connect generative AI systems to business applications for automated workflows while controlling app access and protecting credentials.

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