What is Natural Language Generation?

Natural Language Generation (NLG) is the AI technology that creates human-like text from data or instructions. It's a subfield of Natural Language Processing focused specifically on generating written content rather than understanding it.

When you give an AI writing tool a prompt and it produces a blog post, NLG is generating that text. It's the "writing" part of AI writing tools.

How NLG works

NLG systems use patterns learned from massive text datasets to generate new text that matches those patterns. Large language models like GPT represent advanced NLG - they can generate coherent, contextually appropriate text across diverse topics.

The model predicts the most likely next token based on what came before, then the next, then the next, building up sentences and paragraphs that follow learned patterns of human writing.

NLG doesn't "think" about what to write. It generates statistically likely sequences of words that match patterns it learned during training. This is why it can produce fluent but factually wrong hallucinations.

NLG vs NLP

NLP is the broader field covering all aspects of computers working with human language - understanding, interpreting, AND generating.

NLG is the specific subset focused on generation. When an AI tool reads your prompt, NLP handles understanding. When it writes the response, NLG handles generation.

Both work together in AI writing tools. Understanding what you want (NLP) and generating text that matches (NLG) are complementary capabilities.

Applications in content creation

AI first drafts: NLG generates initial blog post content from prompts and content briefs, creating drafts for human editing.

Content scaling: NLG enables faster content generation, producing more drafts than manual writing allows.

Variation generation: NLG can produce multiple versions of the same content with different phrasings, helping test what resonates with audiences.

Automated summaries: NLG can read long content and generate concise summaries or meta descriptions.

Limitations

NLG generates plausible text, not accurate text. It can't fact-check itself or know when it's producing false information. Hallucinations are a fundamental limitation of current NLG technology.

NLG lacks true understanding. It matches patterns without comprehending meaning, which is why it can generate grammatically perfect nonsense or miss subtle context that humans would catch.

Generated text often sounds generic unless guided by detailed prompts. NLG defaults to common patterns, producing formulaic content without specific instructions to do otherwise.

Quality of NLG output

NLG quality varies significantly based on the model, prompt quality, and temperature settings. Better models like GPT-4 produce more coherent, contextually appropriate text than earlier versions.

However, even the best NLG shouldn't be published raw. Human-in-the-loop editing remains essential to add specifics, fix errors, and ensure content is genuinely helpful.

Future of NLG

NLG technology is rapidly advancing. Models are becoming better at longer context windows, more nuanced following of instructions, and more natural-sounding output.

But the core limitation remains: NLG generates patterns, not understanding. It will likely always require human editors to verify accuracy, add expertise, and ensure content serves readers' needs.

Using NLG effectively

Provide detailed prompts with context, examples, and specific instructions. Better inputs generate better NLG output.

Set appropriate expectations. Use NLG for speed, not finished quality. Treat output as first drafts requiring editing.

Focus on where NLG adds value: generating structure, overcoming blank-page syndrome, creating initial content flow. Then apply human expertise where it matters: adding specifics, ensuring accuracy, injecting unique perspective.