What Is Natural Language Generation?

 Natural language generation is the use of artificial intelligence programming to produce written or spoken language from a data set. It is used to not only create songs, movies scripts and speeches, but also report the news and practice law.

At its best, NLG output can sound so natural that it appears to be produced by a human. This has only been possible for a few years, and “it’s only the tip of the iceberg,” said Jay Alammar, director and engineering fellow at natural language processing company Cohere. “As with every new technology, it takes some time to really understand where this technology excels and where it falls short.”

How Does Natural Language Generation Work?

Natural language generation systems transform raw data into natural-sounding language through a multi-step process often called the NLG pipeline. While approaches vary across rules-based systems and large language models (LLMs), most follow a series of core steps. 

1. Content Analysis: The first step is content analysis, which is where all the data, both structured and unstructured, is analyzed and filtered so that the final text generated addresses the user’s needs. (Structured data is searchable and organized, while unstructured data is in its native form.)
2. Pattern Recognition: The NLG system then has to make sense of that data, which involves identifying patterns and building context. 
3. Data Structuring: Next comes data structuring, which involves creating a narrative based on the data being analyzed and the desired result (blog, report, chat response and so on). 
4. Grammatical Structuring: Through grammatical structuring, the words and sentences are then rearranged so that they make sense in the given language.
5. Aggregation and Formatting: Finally, before the output is produced, it runs through any templates the programmer may have specified and adjusts its presentation to match it in a process called language aggregation.

One result of this process is written natural language. This can come in the form of a blog post, a social media post or a report, to name a few. But there are more use cases.

Common Uses of Natural Language Generation

Personalizing Customer Engagement Materials 

Qualtrics, the experience management software company, has a program in beta called Automated Call Summaries, which can be used in call centers as a way to take and maintain notes about specific customers and their experiences. Qualtrics summarizes calls using two primary approaches: extractive and abstractive.

“Extractive works well when the original body of text is well-written, is well-formatted, is single speaker. It’s highly grammatical, well organized,” Ellen Loeshelle, Qualtrics director of product management, told Built In, adding that it “completely falls apart” when applied to an automatically transcribed conversation, or when informal language is used. 

So, Qualtrics uses a “hybrid” approach, where they have their customers build out a structure or format dictating exactly what they want their summaries to say and how they want them to look, allowing them to then plant in variables that align with a given conversation.

Creating Written Content

NLG is especially useful for producing content such as blogs and news reports, thanks to tools like ChatGPT. ChatGPT can produce essays in response to prompts and even responds to questions submitted by human users. The latest version of ChatGPT, ChatGPT-4, can generate 25,000 words in a written response, dwarfing the 3,000-word limit of ChatGPT. As a result, the technology serves a range of applications, from producing cover letters for job seekers to creating newsletters for marketing teams.

Powering Conversational AI

NLG can also produce natural spoken language in the form of conversational AI  — a common example is AI voice assistants like Amazon’s Alexa, Apple’s Siri or the Google Home Assistant. Interactions with these devices exist solely as conversations, where a user asks a question or makes a statement and the device offers an answer.

Monitoring Industrial IoT Devices

When it comes to interpreting data contained in Industrial IoT devices, NLG can take complex data from IoT sensors and translate it into written narratives that are easy enough to follow. Professionals still need to inform NLG interfaces on topics like what sensors are, how to write for certain audiences and other factors. But with proper training, NLG can transform data into automated status reports and maintenance updates on factory machines, wind turbines and other Industrial IoT technologies.  

Interpreting Graphs, Tables and Spreadsheets

AI art generators already rely on text-to-image technology to produce visuals, but natural language generation is turning the tables with image-to-text capabilities. By studying thousands of charts and learning what types of data to select and discard, NLG models can learn how to interpret visuals like graphs, tables and spreadsheets. NLG can then explain charts that may be difficult to understand or shed light on insights that human viewers may easily miss. 

Types of Natural Language Generation Algorithms

NLG’s improved abilities to understand human language and respond accordingly are powered by advances in its algorithms. Below are four NLG algorithms to keep in mind.     

Markov Chain

The Markov chain is one of the earliest NLG algorithmic models. Within the context of natural language generation, a Markov chain assesses the relationships between current words in a sentence, considers the probability of what the next word could be based on these relationships and then tries to predict the next word in the sentence. Text suggestions on smartphone keyboards is one common example of Markov chains at work.  

Recurrent Neural Network 

Recurrent neural networks mimic how human brains work, remembering previous inputs to produce sentences. For every word in the dictionary, RNNs assign a probability weight. As the text unfolds, they take the current word, scour through the list and pick a word with the closest probability of use. Although RNNs can remember the context of a conversation, they struggle to remember words used at the beginning of longer sentences. As a result, their lengthier sentences tend to make less sense.  

Long Short-Term Memory

Like RNNs, long short-term memory (LSTM) models are good at remembering previous inputs and the contexts of sentences. LSTMs are equipped with the ability to recognize when to hold onto or let go of information, enabling them to remain aware of when a context changes from sentence to sentence. They are also better at retaining information for longer periods of time, serving as an extension of their RNN counterparts.     

Transformer

First introduced by Google, the transformer model displays stronger predictive capabilities and is able to handle longer sentences than RNN and LSTM models. While RNNs must be fed one word at a time to predict the next word, a transformer can process all the words in a sentence simultaneously and remember the context to understand the meanings behind each word. This process makes it faster to generate cohesive sentences.      

NLP vs. NLU vs. NLG

To understand how natural language generation fits into the larger artificial intelligence ecosystem, one must first understand natural language processing (NLP) — a subset of computational linguistics that refers to the use of computers to understand both written and spoken human language. If NLG is a building, NLP is the foundation.

Natural Language Processing

Natural language processing (NLP) uses both machine learning and deep learning techniques in order to complete tasks such as language translation and question answering, converting unstructured data into a structured format. It accomplishes this by first identifying named entities through a process called named entity recognition, and then identifying word patterns using methods like tokenization, stemming and lemmatization.

“I think of natural language processing as very much the foundational technology that makes natural language generation possible.”

In short: Natural language processing is understanding the “pieces” of language, Qualtrics’ Loeshelle said, which is essential to generating language. 

“In order to create language, you have to understand language. You have to understand its component parts, how they work together, what they mean, what it sounds like to be a native speaker,” she explained. “I think of natural language processing as very much the foundational technology that makes natural language generation possible.”

Natural Language Understanding

Natural language understanding (NLU) is another branch of the NLP tree. Using syntactic (grammar structure) and semantic (intended meaning) analysis of text and speech, NLU enables computers to actually comprehend human language. NLU also establishes relevant ontology, a data structure that specifies the relationships between words and phrases. 

Humans are able to do all of this intuitively — when we see the word “banana” we all picture an elongated yellow fruit; we know the difference between “there,” “their” and “they’re” when heard in context. But computers require a combination of these analyses to replicate that kind of understanding.

NLU has many practical applications. One is text classification, which analyzes a piece of open-ended text and categorizes it according to pre-set criteria. For instance, if you have an email coming in, a text classification model could automatically forward that email to the correct department. 

It can also be applied to search, where it can sift through the internet and find an answer to a user’s query, even if it doesn’t contain the exact words but has a similar meaning. A common example of this is Google’s featured snippets at the top of a search page. 

In some cases, natural language understanding also consists of speech recognition. While speech recognition technology captures spoken language in real-time, transcribes it and returns it as text, natural language understanding goes beyond that — determining a user’s intent through machine learning.  

Natural Language Generation

NLG derives from the natural language processing method called large language modeling, which is trained to predict words from the words that came before it. If a large language model is given a piece of text, it will generate an output of text that it thinks makes the most sense.

“If you train a large enough model on a large enough data set,” Alammar said, “it turns out to have capabilities that can be quite useful.” This includes summarizing texts, paraphrasing texts and even answering questions about the text. It can also generate more data that can be used to train other models — this is referred to as synthetic data generation.

But NLP and NLU are equally vital to a successful NLG model. According to the principles of computational linguistics, a computer needs to be able to both process and understand human language in order to general natural language. 

More on Natural Language Processing13 Natural Language Processing Examples to Know

The Future of Natural Language Generation

Natural language generation’s ability to analyze and describe massive amounts of data in a human-like manner at rapid speeds continues to not only dazzle, but stoke ongoing fears of AI’s capacity to take human jobs. But NLG software can be quite beneficial to their human counterparts, Alammar said, particularly when it comes to helping writers scale their work. 

Instead of replacing humans altogether, natural language generation can “help the creative process,” Alammar said. “[NLG] might not necessarily generate the final draft for you, but it can help you brainstorm,” he continued, likening it to a new tool in a toolbox, joining other longtime writer lifesavers like spell check. “This is an extension of this family of writing aids.” 

Like most other artificial intelligence, NLG still requires quite a bit of human intervention. We’re continuing to figure out all the ways natural language generation can be misused or biased in some way. And we’re finding that, a lot of the time, text produced by NLG can be flat-out wrong, which has a whole other set of implications. 

“It can make mistakes. It can generate text that is totally plausible, but is factually incorrect.”

“It can make mistakes. It can generate text that is totally plausible, but is factually incorrect,” Loeshelle said. And with grade school students and news outlets alike beginning to incorporate NLG in their own work, it’s easy to see how natural language generation could lead to fake news generation. “It could go really wrong really fast. … That’s the part that scares me about generative text or imagery or video or audio — there’s no signature to say that this is real or not real,” she continued. “I think that’s a huge challenge that this space is still tackling. How do we ensure its integrity?”

Of course, this doesn’t change the fact that natural language generation has come a long way in a fairly short amount of time and holds exciting possibilities. 

“Natural language generation is going to give us the ability to provide information to everyone in the format that they want to receive it, at the time that they want to receive it, at hopefully a much lower cost,” Loeshelle said. “It’s going to be pretty awesome.”

Why Natural Language Generation Matters?

Natural language generation is transforming how people and organizations communicate, automate and scale their operations.. NLG-based tools like Google’s AI Overviews and ChatGPT are already used by millions of people everyday, and their influence is likely to continue expanding as they improve. This has ballooned the NLG market to a projected valuation of $1.10 billion, with estimates of reaching $5.71 billion by 2032.

NLG enables machines to mimic human language at scale, making it a powerful driver of efficiency. Businesses use it to automate everything from customer support tasks to content generation, while government agencies are using  generative AI to modernize workflows and expand their services. AI companies have secured major contracts with various federal agencies, offering their services for  as little as $1 in the hope of gaining wider adoption in the government. 

Still, the rise of NLG isn’t without challenges. Many organizations struggle with implementation costs or seeing a return on their investment, which are two issues that may shape how the technology matures in the coming years. But these hurdles don’t diminish NLG’s importance, they simply highlight the work that still needs to be done to make it reliable and transformative for organizations.

Key Developments in Natural Language Generation (NLG)

Natural language generation has evolved significantly in recent years, advancing from early prototypes to sophisticated systems that produce human-like text and audio. Below are some of the major milestones in the field recently, providing insights into the progression of NLG technology.

OpenAI Launches GPT-5 (August 2025)

The launch of the latest version of OpenAI’s GPT-5 marked a significant step in NLG capabilities. This model improved upon its predecessors by generating more coherent, contextually accurate text across various applications, including content creation, customer service and data summarization. The model’s ability to understand and replicate human-like conversation set new standards in natural language understanding and generation.

Grok 4 Model Launch (July 2025)

Elon Musk's Grok 4, expanded on the AI chatbot's ability to understand nuanced queries and generate highly context-aware responses. The development of Grok 4 illustrated the growing importance of domain-specific training in fine-tuning NLG models for specific industries like healthcare, finance and customer support.

The Launch of Claude 3.7 Sonnet (February 2025)

Anthropic launched Claude 3.7 Sonnet, an advanced model that incorporated quick response capabilities alongside deep reasoning. Its innovative approach combined faster processing with more precise reasoning, positioning it as a strong competitor in the generative AI landscape. This model also focused on improving safety features to minimize the risk of generating harmful or biased content.

DeepSeek Launches DeepSeek R1 (January 2025)

Chinese startup DeepSeek introduced R1, an open-source model that delivers advanced reasoning and problem-solving at a fraction of the cost of competing models. R1 quickly gained attention for outperforming U.S.-developed systems in certain benchmarks, sparking global debate over AI competitiveness and accessibility. The launch was seen as a “Sputnik moment” in AI, with industry leaders both praising its capabilities and raising concerns over data security and regulatory oversight.

Google Begins Rolling Out AI Overviews (May 2024)

Google launched AI Overviews in Search, providing users with AI-generated summaries at the top of results pages. The feature’s introduction marked a major shift in how people accessed information, as it could quickly analyze web content and answer search queries without having to redirect users off-site.  

Google DeepMind Releases AlphaCode 2 (December 2023) 

AlphaCode 2, launched by DeepMind in 2023, marked a breakthrough in NLG specifically for programming tasks. By generating human-quality code through natural language inputs, it opened the door for automated software development. AlphaCode demonstrated the growing intersection of NLG with other AI fields, like code generation, making it a significant milestone in AI's practical applications.

Launch of ChatGPT (November 2022)

OpenAI’s release of ChatGPT brought NLG into the mainstream. The platform introduced context-aware dialogue and text generation, and quickly gained mass adoption across various industries through consumer and enterprise applications. 

The Debut of GPT-3 and the Birth of Modern NLG (June 2020)

The release of OpenAI’s GPT-3 was a landmark moment for NLG. This model, with 175 billion parameters, showcased the potential of large-scale transformer models in generating fluent, coherent and contextually relevant text. Its widespread application across content creation, marketing and automation laid the groundwork for the current NLG landscape, setting a high bar for future models. 

Google Introduces Transformer Architectures (August 2017)

Google researchers introduced transformer architectures, a breakthrough that became the foundation for modern large language models. The transformer architecture is a neural network design that enables more efficient training and improved handling of long-range dependencies. It works by using a self-attention mechanism, which helps determine the relevance of each word in an input. The method laid the groundwork for many of the recent advances in NLG. 

Early NLG Research and Basic Text Generators (2000s)

In the early 2000s, NLG systems were primarily rule-based and focused on generating reports from structured data. These systems, such as those used in weather forecasting and finance, provided a foundation for more advanced NLG systems. While their outputs were basic, they demonstrated the feasibility of automatic text generation, sparking future advancements in the field.

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