What is nlg?

Natural Language Generation (NLG) is a branch of artificial intelligence that automatically turns structured data-like numbers, facts, or code-into readable, human‑like text. Think of it as a computer writer that can take raw information and produce sentences, paragraphs, or full reports.

Let's break it down

NLG works in a few clear steps:

  • Data Input: The system receives raw data (e.g., sales numbers, sensor readings).
  • Content Determination: It decides what information is important enough to mention.
  • Document Structuring: It organizes the chosen facts into a logical order (introduction, body, conclusion).
  • Lexicalisation: It picks the right words and phrases to express each fact.
  • Aggregation: It combines short sentences into smoother, longer ones.
  • Linguistic Realisation: It applies grammar, punctuation, and style rules to produce the final text.

Why does it matter?

NLG saves time and effort by automating repetitive writing tasks, making large data sets understandable for people who aren’t data experts. It helps businesses scale communication, improves consistency across documents, and can personalize messages for each reader without extra manual work.

Where is it used?

  • Automated weather and traffic reports
  • Financial earnings summaries and stock analysis
  • Customer‑service chatbots and virtual assistants
  • Email or newsletter generation for marketing campaigns
  • Game dialogue and story generation
  • Medical discharge summaries and health‑record explanations

Good things about it

  • Speed: Generates reports in seconds instead of hours.
  • Consistency: Keeps tone and format uniform across all outputs.
  • Scalability: Handles thousands of documents with the same effort.
  • Personalisation: Can tailor language to individual users or contexts.
  • Cost‑effective: Reduces the need for large writing teams for routine content.

Not-so-good things

  • Quality depends on data: Bad or biased input leads to inaccurate or unfair text.
  • Lack of nuance: May miss subtle humor, sarcasm, or cultural references.
  • Potential for errors: Mistakes in grammar or fact selection can slip through.
  • Over‑automation risk: Over‑reliance can reduce human oversight and creativity.
  • Complex setup: Building a high‑quality NLG system often requires expert knowledge and fine‑tuning.