Natural Language Generation: Definition and Examples

Natural Language Generation (NLG) refers to the set of AI techniques that allow a computer system to produce text understandable by a human being. Unlike Natural Language Understanding (NLU), which aims to understand language, NLG focuses on producing coherent, grammatically correct, and contextually relevant text.

Historically, early NLG systems relied on templates (predefined models) and manually coded linguistic rules. These approaches produced rigid and predictable texts, such as automated weather bulletins or financial summaries. The advent of neural networks, then transformers, radically transformed the field: language models like GPT or Claude are capable of generating fluid, nuanced, and creative text from a simple natural language instruction.

In prompt engineering, NLG is at the heart of every interaction with an LLM. Each response generated by the model is an act of NLG. The quality of the produced text directly depends on the quality of the prompt: a precise, well-structured, and contextualized prompt guides the model toward more relevant and useful generation.

Modern applications of NLG span an immense spectrum: writing articles, generating code, creating chatbot dialogues, machine translation, document summarization, personalizing marketing content, and much more. Today, it is one of the most visible and widely used capabilities of generative artificial intelligence.