NLP

Natural Language Generation (NLG)

An AI's ability to write, producing human-readable text from data, templates, or learned patterns.

Definition

The subfield of NLP focused on producing coherent, contextually appropriate text from structured data, prompts, or learned patterns. Tasks range from templated report writing to open-ended generation. Early NLG used rule-based templates that filled in blanks; modern NLG is driven by transformer language models that generate fluent text one token at a time, conditioned on the input and the words already produced.

Why it matters

NLG automates writing at scale: drafting chatbot replies, turning dashboards into plain-English summaries, personalizing outbound messages, and generating product descriptions. The trade-off is oversight, since fluent output can still be factually wrong, which is why human review and guardrails matter.

Related terms in NLP

BERT

Google's breakthrough AI model that reads sentences in both directions at once to understand context better.

Chain of Thought (CoT)

Asking an AI to "show its work" and think step-by-step, which makes it much better at solving math and logic problems.

Context Window

The maximum amount of text an AI can read and consider at one time, like how many pages of notes it can hold in its head.

Conversational AI

AI that can have natural back-and-forth conversations with humans, chatbots, voice assistants, and customer service bots.

Back to the full glossary

From vocabulary to outcomes

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