Natural Language Generation In A Nutshell

Natural Language Generation (NLG) is a form of artificial intelligence that generates natural language from structured data. NLG is a software process that automatically transforms data into plain-English content. The content is written as a narrative by the technology, replete with sentences and paragraphs.

Understanding Natural Language Generation

Natural Language Generation is a rapidly growing field that has seen great popularity among businesses. While NLG has an endless array of applications, it is useful for time or resource-intensive activities where is a need to generate content from data at scale.

Such applications include:

  • Written analysis for business intelligence dashboards.
  • App or email-based customer communication.
  • Client portfolio updates and summaries.
  • Landing page content and eCommerce product descriptions.
  • Internet of things (IoT) device maintenance and status reporting.

Four processes of Natural Language Generation architecture

NLG could never replicate the text generated by a real person, but it does use a range of methods to adapt its writing style according to the tone, structure, context, and purpose of the narrative. 

To clarify these methods, researchers must define these processes:

  1. Document planning – to determine what should be said, an abstract document is created based on the knowledge of the user. Information must also consider the goals of both the writer and reader.
  2. Sentence planning – what are the referring expressions or word choices? How will the sentences and paragraphs be structured? This step is sometimes called microplanning and involves techniques such as referring expressions, aggregation, grammaticalization, and lexicalization. 
  3. Surface realization – or the generation of grammatically correct sentences using proper syntax and inflection.
  4. Physical presentation – depending on whether the information is written or spoken, the text must contain the right articulation, layout, or punctuation.

Natural Language Generation and Natural Language Processing

Natural Language Generation can write information, but it cannot read it. 

This is where Natural Language Processing (NLP) comes in. NLP systems can “read” information in the sense that they can look at human language and determine what ideas are being communicated. Note that ideas are not communicated by words alone. Context, body language, and intonation are also vital in gauging the intent of the spoken word.

In this way, NLP systems incorporate ideas from computer science and computational linguistics to bridge the gap between nuanced human communication and computer understanding.

Real-world applications of NLP

Many of us encounter NLP during our lives without realizing it. Here are some of the more interesting applications:

  1. Virtual assistants such as Siri, Amazon Echo, and Google Home.
  2. Email assistants that correct grammar or filters determine which emails are likely to be spam and which should be sent to the inbox.
  3. Chatbots that answer customer service inquiries on eCommerce sites in real-time.
  4. Aircraft maintenance, where NLP is being used to find meanings in the verbal and written descriptions of aircraft problems given by pilots.
  5. Predictive police work. Although in its infancy, NLP is being used to assist detectives in determining the motives for crimes based on the language of the offender.

Key takeaways:

  • Natural Language Generation is a type of artificial intelligence that generates natural language from structured data.
  • Natural Language Generation uses four key processes to reconstruct the context, tone, structure, and purpose of a narrative or story. These processes are document planning, sentence planning, surface realization, and physical presentation.
  • While Natural Language Generation can write information, Natural Language Processing can read it. Through detailed analysis of written and verbal information, NLP has several interesting and important applications.

Key Highlights:

  • Natural Language Generation (NLG):
    • NLG is an AI technology that converts structured data into human-readable natural language content.
    • It automates the process of generating narratives, paragraphs, and sentences from data, making it valuable for creating content at scale.
  • Applications of NLG:
    • NLG finds applications in various industries and activities, including:
      • Written analysis for business intelligence.
      • Customer communication via apps or emails.
      • Client portfolio updates.
      • Landing page content and product descriptions.
      • IoT device maintenance and status reporting.
  • Four Processes of NLG Architecture:
    • Document planning: Determining the content based on user knowledge and goals.
    • Sentence planning (microplanning): Structuring sentences, choosing expressions, and managing aggregation, grammar, and lexicalization.
    • Surface realization: Generating grammatically correct sentences with proper syntax and inflection.
    • Physical presentation: Formatting and presenting the content based on its intended medium (written or spoken).
  • NLG and Natural Language Processing (NLP):
    • NLG generates information but cannot read it.
    • NLP systems “read” and interpret human language to understand ideas, context, and intent.
    • NLP bridges the gap between nuanced human communication and computer understanding.
  • Real-world Applications of NLP:
    • NLP is widely used in various applications:
      • Virtual assistants like Siri, Amazon Echo, and Google Home.
      • Email assistants for grammar correction and spam filtering.
      • Real-time chatbots for customer service on eCommerce platforms.
      • Aircraft maintenance to understand pilot descriptions of issues.
      • Predictive police work, assisting detectives in understanding criminal motives through language analysis.

Connected Business Model Analyses

AI Paradigm




Large Language Models

Large language models (LLMs) are AI tools that can read, summarize, and translate text. This enables them to predict words and craft sentences that reflect how humans write and speak.

Generative Models


Prompt Engineering

Prompt engineering is a natural language processing (NLP) concept that involves discovering inputs that yield desirable or useful results. Like most processes, the quality of the inputs determines the quality of the outputs in prompt engineering. Designing effective prompts increases the likelihood that the model will return a response that is both favorable and contextual. Developed by OpenAI, the CLIP (Contrastive Language-Image Pre-training) model is an example of a model that utilizes prompts to classify images and captions from over 400 million image-caption pairs.

OpenAI Organizational Structure

OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.

OpenAI Business Model

OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.


OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem


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