What is Natural Language Generation (NLG)
- Muiz As-Siddeeqi
- Oct 12
- 27 min read
Every second, machines transform billions of data points into words you can read and understand. Financial reports write themselves. Product descriptions appear instantly across thousands of items. Medical summaries generate from patient records in moments. This isn't science fiction—it's Natural Language Generation at work, quietly revolutionizing how businesses communicate at scale.
TL;DR
NLG transforms structured data into human-readable text using AI and machine learning algorithms
The global NLG market reached $655 million in 2023 and is projected to grow at 21.8% CAGR through 2030 (Grand View Research, 2024)
Associated Press increased earnings coverage from 300 to 4,400 stories per quarter using NLG technology (Automated Insights, 2014)
Applications span finance, healthcare, e-commerce, journalism, and customer service with measurable ROI
Key challenges include hallucination, bias, and context understanding, though mitigation methods continue improving
Rule-based and neural approaches offer different trade-offs between control and fluency
Natural Language Generation (NLG) is an artificial intelligence technology that automatically converts structured data into natural human language. It analyzes data patterns, applies linguistic rules, and produces coherent text—from simple reports to complex narratives. NLG powers applications like automated journalism, personalized product descriptions, clinical documentation, and financial reporting, enabling businesses to create thousands of unique text pieces in seconds.
Table of Contents
What is Natural Language Generation?
Natural Language Generation represents a transformative subset of artificial intelligence focused on converting raw, structured data into coherent, natural-sounding human language. Unlike systems that simply template-fill or mail-merge content, genuine NLG analyzes data relationships, understands context, and constructs grammatically correct narratives that read as if written by humans.
Think of NLG as a translator—but instead of converting French to English, it converts rows in a database into sentences in a report. When a company's sales system shows revenue increased 23% quarter-over-quarter in the Northeast region, an NLG system transforms those numbers into: "The Northeast region delivered strong performance this quarter, with revenue climbing 23% compared to the previous period, driven primarily by increased demand in the enterprise segment."
The technology emerged from natural language processing research in the 1960s, but practical applications accelerated dramatically after 2010 with advances in machine learning. Today, NLG systems range from template-based tools generating simple weather reports to sophisticated neural networks crafting nuanced content across 110+ languages.
How NLG Works: The Technical Foundation
Natural Language Generation operates through a multi-stage pipeline that transforms data into text. While implementations vary, most systems follow this general architecture:
Content Determination
The system first decides what information from the dataset deserves inclusion. Not every data point matters equally. For a sports game recap, final score and star player statistics matter more than individual pitch counts. This stage involves:
Identifying salient data points
Determining relevance thresholds
Filtering noise and redundant information
Prioritizing based on user needs or business rules
Document Planning
Once content is selected, the system organizes information logically. This mirrors how human writers outline before drafting. The system determines:
Overall narrative structure (chronological, importance-based, comparative)
Paragraph and section boundaries
Information flow and transitions
Rhetorical strategies (explanatory, persuasive, descriptive)
Sentence Aggregation
Raw facts get grouped into coherent sentences. Rather than stating "Revenue was $5M. Revenue increased 15%," the system combines: "Revenue reached $5M, representing a 15% increase." This stage handles:
Combining related facts
Avoiding repetition
Varying sentence structure
Managing pronouns and references
Lexicalization
The system selects specific words and phrases. The same concept can be expressed multiple ways: "revenue grew," "revenue increased," "revenue expanded," "sales climbed." Lexicalization considers:
Vocabulary appropriate to audience (technical vs general)
Brand voice and tone consistency
Avoiding word repetition
Emotional connotation
Linguistic Realization
Finally, the system applies grammar rules to produce grammatically correct, fluent text. This includes:
Subject-verb agreement
Tense consistency
Proper punctuation
Morphological variations (run/ran/running)
Modern NLG systems use two primary technical approaches to execute this pipeline. Rule-based systems follow explicit linguistic rules defined by developers. Neural systems, particularly those based on the transformer architecture introduced by Google researchers in 2017, learn patterns from massive text datasets (AWS, 2024).
The transformer architecture revolutionized NLG by using attention mechanisms that process entire sequences simultaneously rather than word-by-word. This enables models like GPT-3, which has 175 billion parameters trained on 45 terabytes of text data (OpenAI, 2020), to generate remarkably human-like content. GPT-4, released in March 2023, pushed capabilities further with multimodal understanding, processing both text and images (Wikipedia, 2025).
NLG vs NLP vs NLU: Understanding the Differences
These three acronyms often confuse newcomers, but they represent distinct concepts within AI language technology:
Natural Language Processing (NLP) serves as the umbrella term encompassing all AI efforts to understand and work with human language. NLP includes speech recognition, language translation, sentiment analysis, and text generation. Think of NLP as the entire field of study.
Natural Language Understanding (NLU) represents the comprehension side—teaching machines to interpret human language. When you ask Alexa about tomorrow's weather, NLU processes your question, identifies the intent (weather forecast), extracts entities (time: tomorrow), and understands context. NLU is interpretive, deriving meaning from input (Macgence, 2025).
Natural Language Generation (NLG) handles the production side—enabling machines to create human language output. NLG takes data, analysis, or understanding and expresses it in readable text. If NLU reads and comprehends, NLG writes and explains.
Example in Practice:
A customer service chatbot demonstrates all three:
NLP manages the overall system
NLU interprets the customer's question: "Where is my order?"
NLG generates the response: "Your order shipped yesterday and should arrive by Thursday. Here's your tracking number."
The NLG Market: Growth and Statistics
The Natural Language Generation market shows explosive growth across multiple industry reports, reflecting rapid enterprise adoption:
Market Size and Projections
The global natural language generation market was valued at $655.3 million in 2023 and is projected to grow at a CAGR of 21.8% from 2024 to 2030 according to Grand View Research.
Multiple forecasts converge on robust growth trajectories:
The market is expected to reach $2.32 billion by 2029 per The Business Research Company projections.
Key Growth Drivers
Growing industry adoption of AI and machine learning, increasing reliance on data-driven decision making, and increasing usage of analytics and business intelligence applications are major driving factors for market growth (Grand View Research, 2024).
Gartner expects that by 2025, 80% of data and analytics will incorporate automated content generation according to Straits Research.
In February 2023, G2.com reported that 87.8% of companies had increased their data investments, marking a 41% rise from 2022 (Globe Newswire, 2024). This data explosion directly fuels NLG demand—more data requires better ways to communicate insights.
Market Segmentation
By Deployment:
The cloud segment dominated with 67.0% market share in 2023, driven by quick setup, low operational costs, and flexible pricing models (Grand View Research, 2024). On-premises deployment is growing fastest as organizations seek data control and regulatory compliance.
By Enterprise Size:
Large enterprises dominated the market accounting for 67.4% share in 2023 (Grand View Research, 2024). However, software-as-a-service models increasingly democratize NLG access for smaller businesses.
By Application:
Risk and compliance management accounted for the largest market share at 23.0% in 2023, with fraud detection and anti-money laundering segments expected to grow fastest (Grand View Research, 2024).
By Industry:
The BFSI (Banking, Financial Services, and Insurance) segment dominated with 21.8% market share in 2023 (Grand View Research, 2024), followed by healthcare, retail, and media.
Geographic Distribution
North America dominated the NLG market with approximately 40% of total revenue in 2023, followed by Europe at 30% (Verified Market Reports, 2025). Asia-Pacific is expected to be the fastest-growing region in the forecast period (Globe Newswire, 2024).
Real-World Applications and Industry Use Cases
Natural Language Generation delivers measurable value across diverse industries. Here's where it creates impact:
Financial Services
Banks and investment firms leverage NLG extensively for automated report generation. NLG can evaluate financial data and produce narratives such as reports, summaries, and investment insights. Financial institutions use NLG to provide individualized investment reports for clients that summarize portfolio performance, market trends, and data-driven recommendations (Grand View Research, 2024).
Applications include:
Quarterly earnings reports
Portfolio performance summaries
Market analysis briefs
Regulatory compliance documentation
Personalized investment recommendations
Risk assessment narratives
Healthcare and Medical
According to a report by Stats Research Market, the global Healthcare NLP market is projected to be valued at $886.94 million in 2024 and expected to grow to $1083.97 million by 2029, reflecting a CAGR of 3.40% (Veritis, 2025).
In 2024, the U.S. NLP in the healthcare & life sciences market reached approximately $1.44 billion, and it's projected to balloon to ~$14.7 billion by 2034, growing at a 26% CAGR (Veritis, 2025).
Medical applications include:
Clinical documentation automation
Patient discharge summaries
Radiology report generation
Medical coding assistance
Clinical trial matching narratives
Patient communication materials
Healthcare sector can use NLG to make clinical documentation easier, giving healthcare professionals access to more accurate information to make decisions with and reducing the risk of errors in documentation (Cogent Infotech, 2024).
E-Commerce and Retail
Online retailers face the daunting task of creating unique, compelling product descriptions for thousands or millions of items. AX Semantics, an AI-powered natural language generation leader, helps online retailers solve one of ecommerce's biggest pain points: the ability to create vast quantities of unique product descriptions in multiple languages at scale (AX Semantics, 2024).
Companies like Porsche, Adidas, MyTheresa.com, Nestlé, and Nivea use automated description generation. According to billiger.de, the company now provides visitors with detailed guides, offering insights such as product advantages and disadvantages, essential information, and special tips using Epic Product Descriptions from AX Semantics (AX Semantics, 2024).
AKKU SYS GmbH generated more than 33,000 unique product descriptions in just 2 months using AX Semantics' text automation software (AX Semantics, 2024).
Journalism and Media
News organizations adopted NLG early to scale coverage. Automated Insights produced 300 million pieces of content in 2013, which Mashable reported was greater than the output of all major media companies combined. In 2014, the company's software generated one billion stories. In 2016, Automated Insights produced over 1.5 billion pieces of content (Wikipedia, 2024).
Beyond earnings reports (detailed in the case study below), media applications include:
Sports game recaps
Weather reports
Election results
Real estate listings
Fantasy sports content
Local news stories
Customer Service
Chatbots and virtual assistants use NLG to generate responses that feel natural and contextually appropriate. According to Forrester, 65% of enterprises already use NLG tools in at least one business function (Macgence, 2025).
NLG enables chatbots to deliver personalized user experience for resolution of queries, booking complaints, or virtual assistance for processes done online, enabling businesses to enhance their customer experience (Grand View Research, 2024).
Business Intelligence and Analytics
NLG automates the creation of performance reports, sales summaries, and dashboards, with one CIO from a Global Retail Chain reporting they "cut reporting time by 80% using NLG-powered tools" (Macgence, 2025).
Case Study: Associated Press Transforms Financial Reporting
The Associated Press partnership with Automated Insights represents one of the most documented and impactful NLG deployments in journalism.
The Challenge
AP reporters spent significant time and effort manually gleaning insights from quarterly financial reports released by public companies in the US. Owing to limited time and manual resources, AP reporters produced only 300 such articles every quarter, leaving out thousands of potential companies that published their quarterly corporate earnings (Emerj, 2024).
Manual financial reporting involved extracting data on profit, revenue growth, tax expenses, and other metrics, then transforming those numbers into coherent financial recaps. The process consumed substantial journalist time while covering less than 10% of publicly traded companies.
The Solution
In June 2014, The Associated Press announced it would use automation technology from Automated Insights to produce most of its U.S. corporate earnings stories, with AP saying automation would boost its output of quarterly earnings stories nearly fifteen-fold (Wikipedia, 2024).
AP employed Automated Insights' natural language generation platform, Wordsmith, to auto-summarize the quarterly financial recaps. This NLG platform was configured to write according to the editorial standards of AP (Emerj, 2024).
The configuration process involved:
Feeding AP's editorial rules into Wordsmith
Loading relevant financial data from Zacks Investment Research
Creating templates aligned with AP's style guide
Iterating to refine output quality
Setting up automated workflows
According to Lou Ferrara, AP's VP of Business News, "Our team worked very hard to make sure that the templates we built with Automated Insights met AP standards and style but also read like earnings stories" (Automated Insights, 2024).
The Results
According to Automated Insights, the number of published financial recaps at AP rose from 300 to 4,400 per quarter, resulting in a 12-fold increase. The company claims that while its NLG platform hasn't displaced any reporters, it has freed up the equivalent of three full-time employees across AP (Emerj, 2024).
Now, using the Wordsmith platform, the Associated Press produces 3,700 corporate earnings stories per quarter (Automated Insights, 2024).
The broader impact extended beyond volume. A study by researchers at Stanford and the University of Washington found that Automated Insights' technology has affected the stock market, as firms that received little attention from traders now see significant increases in trade volume and liquidity (Wikipedia, 2024; Automated Insights, 2024).
Before the partnership, the AP could only cover around 300 firms. With Wordsmith, the AP can now cover around 4,500 firms each quarter (Automated Insights, 2024). This democratized financial coverage, giving smaller companies media attention they never received before.
Quality remained high. Academic studies have shown that readers cannot distinguish the content from a Wordsmith user's template from articles written manually by journalists (Marketing AI Institute, 2022).
AP later expanded its use of Wordsmith to automate over 9,000 Minor League Baseball game recaps per year. Slate reviewed the stories noting "Automated Insights' software is significantly more sophisticated than [Madlibs]" (Automated Insights, 2024).
Case Study: E-Commerce at Scale with AX Semantics
E-commerce companies face unique content challenges—thousands of products requiring unique, compelling, SEO-optimized descriptions in multiple languages.
The Problem
Manual copywriting doesn't scale for large product catalogs. A single copywriter might produce 10-20 quality descriptions per day. For a retailer with 50,000 SKUs, that's over two years of full-time writing. Product launches delay. SEO suffers from duplicate content. Seasonal updates become impossible.
Translation compounds the problem. A retailer operating across European markets needs descriptions in German, French, Spanish, Italian, Dutch, and more—multiplying content requirements by language count.
The Solution
AX Semantics launched globally in December 2019 with their AI-powered, natural language generation software used within the e-commerce, business, finance, and media publishing sectors. The software helps make automated content generation accessible to ecommerce companies of all sizes (AX Semantics, 2024).
AX Semantics NLG software supports 110 languages, allowing easy implementation of multilingual projects. All you need to do is translate the content parts. Logics and rules can be taken from the source language (AX Semantics, 2024).
The platform works through a data-to-text approach:
Connect product database (JSON, CSV, API integration)
Define content structure and rules
Set brand voice parameters
Configure variations for diversity
Generate thousands of descriptions with one click
Auto-update when product data changes
The Results
Vanessa Wurster, Team Lead E-Commerce at AKKU SYS GmbH, reported: "Thanks to AX Semantics' text automation software, we've generated more than 33,000 unique product descriptions in just 2 months" (AX Semantics, 2024).
The billiger.de team transitioned to a "product advisor" model, providing visitors with detailed guides including product advantages and disadvantages, essential information, and special tips, adding significant value to the user experience (AX Semantics, 2024).
Key benefits reported by users:
Time savings: Hours instead of months for full catalog coverage
Consistency: Brand voice maintained across all products
SEO improvement: Unique content avoids duplicate content penalties
Multilingual reach: Simultaneous generation in 110+ languages
Cost reduction: Eliminates outsourcing to freelance writers
Real-time updates: Descriptions refresh when product data changes
The difference between data-to-text and GPT-3 NLG is that with data-to-text, humans configure rules and statements once in advance, and they do not need to be checked by humans in post-processing. With GPT-3, human review is required (AX Semantics, 2024).
Case Study: Healthcare Documentation and Clinical Reports
Healthcare organizations drown in documentation requirements. Physicians spend up to 50% of their time on paperwork rather than patient care. NLG offers relief.
The Need
72% of healthcare firms automated clinical documentation, 65% use NLP for EHR mining, delivering 67% improvement in documentation efficiency and 63% reduction in manual entry (Veritis, 2025).
Clinical documentation demands include:
Patient discharge summaries
Clinical visit notes
Radiology report generation
Medication reconciliation
Care plan narratives
Quality measure reporting
Implementation Approach
The National Health Service in the United Kingdom developed a first-of-its-kind clinical NLP service using parallel harmonised platforms. They amassed over 26,086 annotations spanning 556 SNOMED CT concepts working with secondary care specialties (BMC Medical Informatics, 2024).
Their integrated language modelling service has delivered numerous clinical and operational use-cases using named entity recognition (NER) (BMC Medical Informatics, 2024).
The system extracts data from electronic health records, identifies relevant clinical concepts, and generates structured reports following medical terminology standards.
Impact on Healthcare Delivery
Trends and similarities in clinical texts correlate with risk of future medical complications and hospitalization. With NLP of these textual bodies, predictive models can be created, scanning patient clinical data and forecasting admission into medical facilities (PMC, 2024).
One study focusing on accurate prediction of mortality outcomes in ICU patients found that the combination of NLP-derived keywords and terms consistently enhanced model performance and increased the area under the receiver operating characteristic curve (AUC) from 0.831 to 0.922 (PMC, 2024).
NLG systems can quickly extract relevant information from patient records to identify trends or correlations in patient data, which can then be used to better understand patients' health and inform healthcare decisions (Cogent Infotech, 2024).
Benefits realized:
Reduced documentation time allowing more patient interaction
Improved consistency in clinical note quality
Better coding accuracy for billing
Enhanced data availability for research
Standardized terminology use
Reduced physician burnout
The adoption of NLP solutions in the healthcare and life sciences market is expected to increase from $2.2 billion in 2022 to $7.2 billion by 2027 at a CAGR of 27.1% according to Cogent Infotech.
Types of NLG Systems: Rule-Based vs Neural
Natural Language Generation systems fall into two main categories, each with distinct advantages and trade-offs:
Rule-Based (Template-Based) NLG
Also called "data-to-text" or "deterministic" NLG, these systems follow explicitly defined linguistic rules and templates created by developers.
How It Works:
Developers write templates with variables: "{Company} reported {metric} of {value}, {comparison} from {previous_period}"
Rules determine word choice based on data: if revenue_change > 10%: use "surged", elif > 5%: use "increased", else: use "grew slightly"
Grammar rules ensure correctness
System fills templates with actual data values
Advantages:
Full control: Output is predictable and consistent
No hallucination: System cannot invent facts not in the data
Domain-specific: Can be finely tuned to industry terminology
Transparent: Easy to understand and debug
No training data needed: Rules are hand-crafted
Disadvantages:
Labor-intensive setup: Requires significant initial development
Limited flexibility: Hard to handle truly novel scenarios
Scalability challenges: Complex content requires extensive rules
Less natural fluency: Can feel formulaic with simple templates
Best For:
Financial reporting with strict accuracy requirements
Product descriptions following consistent formats
Compliance documentation
Data dashboards and analytics
Any application where factual precision matters more than creative prose
According to AnalyticsInsights, Yseop, a company with more than 100 employees, is the largest company in the rule-based NLG domain (AIMultiple, 2024).
Neural (Machine Learning-Based) NLG
Neural systems, particularly those using transformer architectures, learn language patterns from vast text corpora rather than following explicit rules.
How It Works:
Model trains on billions of words from books, websites, articles
Learns statistical patterns of how words relate and combine
Uses attention mechanisms to maintain context
Generates text by predicting most likely next word repeatedly
Advantages:
High fluency: Generates natural, human-like text
Versatile: Handles diverse topics and formats
Learns from examples: Improves with more training data
Handles complexity: Manages nuanced, creative content
Context-aware: Maintains coherence across long passages
Disadvantages:
Hallucination risk: May generate plausible-sounding but false information
Less controllable: Harder to guarantee specific outputs
Requires training data: Needs massive datasets
Computational cost: Training and running large models is expensive
Bias potential: Reflects biases in training data
Explainability: Difficult to understand why specific text was generated
Best For:
Creative writing and marketing copy
Conversational AI and chatbots
Content requiring varied expression
Scenarios where some creative liberty is acceptable
General-purpose text generation
A Stanford 2023 study found that 23% of generated texts from LLMs contained minor inaccuracies (Macgence, 2025).
87% of enterprises using NLG for regulated sectors rely on human-in-the-loop systems, where humans guide, refine, and review machine-generated text according to McKinsey 2024.
Hybrid Approaches
Leading platforms increasingly combine both methods:
Use templates for structure and factual content
Apply neural models for fluency and variation
Implement human review for high-stakes content
Leverage rules to constrain neural output
AX Semantics' axite platform uses hybrid KI-Architecture (GenAI + regelbasiertes NLG) to create content that is immediately ready for use, brand-compliant, and available in any language (AX Semantics, 2025).
Benefits and Advantages of NLG
Organizations implementing NLG systems report measurable improvements across multiple dimensions:
Speed and Scale
A human can write a thousand words per hour, while automated content creation software can write the same amount in seconds (AX Semantics, 2024). This speed advantage enables previously impossible content volumes.
Wordsmith empowers organizations to produce content at a scale humanly impossible, creating millions of narratives in a fraction of the time it would take to manually craft each one (Automated Insights, 2024).
Cost Efficiency
Manual content creation carries significant costs:
Copywriter salaries for internal teams
Freelancer fees at $0.10-$0.50 per word
Translation services multiplied by language count
Opportunity cost of delayed product launches
Hiring humans to turn data into texts is both time-consuming and expensive. NLG software can do the job faster and cheaper (AX Semantics, 2024).
Consistency and Quality
Data-to-text breaks all the natural boundaries that apply to detailed product communication. Resource bottlenecks and administrative complexity for many product texts are no longer a problem (AX Semantics, 2024).
Brand voice remains consistent across thousands of pieces. Terminology usage follows standards. Updates propagate instantly.
Personalization at Scale
Wordsmith uses each person's unique set of data to personalize messaging and create content that speaks to their individual interests, roles, and responsibilities (Automated Insights, 2024).
Amazon and Netflix utilize Natural Language Generation to provide users with exceptionally tailored experiences through personalized recommendations and product descriptions (Straits Research, 2024).
Multilingual Reach
AX Semantics NLG software supports 110 languages, so you can easily implement a multilingual project (AX Semantics, 2024). Generate content simultaneously in German, French, Spanish, Japanese, Arabic, and dozens more languages from a single source.
Data-Driven Insights
NLG forces organizations to structure their data properly. Creating automated reports reveals data quality issues and gaps, improving overall data management.
Employee Satisfaction
While NLG platforms haven't displaced reporters, they have freed up the equivalent of three full-time employees to focus on higher-value journalism (Emerj, 2024). Employees shift from tedious data entry to strategic work.
Challenges and Limitations
Despite impressive capabilities, Natural Language Generation faces significant challenges that organizations must address:
Hallucination
Deep learning based generation is prone to hallucinate unintended text, which degrades system performance and fails to meet user expectations in many real-world scenarios (arXiv, 2024).
Hallucinations occur when NLG systems generate plausible-sounding but factually incorrect content. Semantic hallucinations pose a challenge in NLG models, leading to inaccurate outputs despite fluency (Linnk AI, 2024).
Types of hallucination:
Intrinsic: Contradicts source data directly
Extrinsic: Adds information not present in source data (may or may not be factually correct)
Like its predecessors, GPT-4 has been known to hallucinate, meaning that the outputs may include information not in the training data or that contradicts the user's prompt (Wikipedia, 2025).
Mitigation approaches include:
Human review for high-stakes content
Retrieval-augmented generation (grounding in verified sources)
Fact-checking modules
Conservative generation parameters
Knowledge graph integration
Knowledge Graphs provide a structured collection of interconnected facts and offer a promising approach to mitigate hallucinations in LLMs, enhancing their reliability and accuracy (ScienceDirect, 2024).
Context Understanding
One challenge encountered by NLG systems is the intricate comprehension of context and the management of language that can have several interpretations. The complexity of human language arises from its delicate contextual clues and the presence of many meanings (Straits Research, 2024).
Research published in the Journal of Artificial Intelligence Research revealed that NLG systems frequently have difficulties effectively learning and producing text in situations with several viable interpretations (Straits Research, 2024).
Bias and Fairness
Training data reflects societal biases around gender, race, age, and other factors. Models learn and potentially amplify these biases.
Researchers noted that failing to account for biases in the development and deployment of an NLP model can negatively impact model outputs and perpetuate health disparities (TechTarget, 2024).
Organizations must:
Audit training data for bias
Test outputs across diverse scenarios
Implement fairness constraints
Maintain diverse development teams
Monitor deployed systems continuously
Data Quality and Availability
NLP shares one major limitation with AI, ML and other advanced analytics technologies: data access and quality. The availability of appropriate and high-quality data is key to training NLP tools (TechTarget, 2024).
Poor data quality leads to poor outputs. Garbage in, garbage out applies fully. NLG systems require:
Structured, clean data
Complete attribute coverage
Consistent formatting
Regular updates
Domain-appropriate metadata
Implementation Complexity
One significant restraint is the complexity associated with implementing NLG systems (Verified Market Reports, 2025).
Successful deployment requires:
Technical infrastructure (APIs, data pipelines)
Domain expertise for rule creation or training
Integration with existing systems
Change management for user adoption
Ongoing maintenance and refinement
Evaluation Difficulty
In the rapidly evolving domain of Natural Language Generation evaluation, introducing Large Language Models has opened new avenues for assessing generated content quality, including coherence, creativity, and context relevance (ACL Anthology, 2024).
Traditional metrics like BLEU and ROUGE measure word overlap but miss semantic quality. Traditional non-LLM automated evaluations have fallen short, failing to consistently match the rigor of human evaluation rubrics. These metrics frequently overlook hallucinations, fail to assess reasoning quality, and struggle to determine the relevance of generated texts (Nature, 2025).
NLG Best Practices and Implementation
Organizations successfully deploying NLG follow these proven practices:
Start with Clear Use Cases
Identify specific problems NLG solves:
Which content creation tasks are repetitive?
Where does manual writing create bottlenecks?
What content requires frequent updates?
Which processes demand perfect consistency?
Ensure Data Readiness
A system that gave "added value" to an existing patient record system would be more persuasive than a stand-alone system requiring separate or idiosyncratic data entry (PMC, 1997).
Before implementing NLG:
Audit data completeness
Standardize formats and schemas
Create data dictionaries
Establish data governance
Build reliable pipelines
Choose the Right Approach
Match NLG type to use case:
Rule-based for factual, structured, compliance-critical content
Neural for creative, varied, conversational content
Hybrid for complex applications requiring both accuracy and fluency
Implement Human Oversight
A hybrid approach, where humans guide, refine, and review machine-generated text, strikes a balance between speed and quality (Macgence, 2025).
Create review workflows with:
Pre-generation: Define data points and rules
Post-generation: Editors refine before publication
Spot-checking: Random sample review
Exception handling: Flag unusual outputs
Feedback loops: Improve system based on issues found
Establish Quality Metrics
Define success measurements:
Accuracy: Factual correctness
Fluency: Grammatical and natural-sounding
Relevance: Appropriate to context
Completeness: All key information included
Consistency: Brand voice maintained
Diversity: Avoiding repetitive phrasing
Plan for Scale
AX Semantics is designed to support any scale, from brands with thousands of products to large retailers with hundreds of thousands of products and language variants (AX Semantics, 2024).
Architect for growth:
Use cloud infrastructure for elasticity
Automate testing and deployment
Build monitoring and alerting
Create feedback mechanisms
Document rules and decisions
Address Ethical Considerations
Transparency matters. The Associated Press is the first newsroom to have an automated editor to oversee automated articles (Wikipedia, 2024).
Best practices include:
Disclose when content is AI-generated (where appropriate)
Maintain editorial oversight for published content
Test for bias regularly
Respect data privacy
Follow industry-specific regulations
The Future of Natural Language Generation
Natural Language Generation stands at an inflection point with several emerging trends:
Multimodal Generation
These systems combine text, visuals, and audio, allowing the creation of rich, multi-sensory content experiences (Macgence, 2025).
On May 13, 2024, OpenAI introduced GPT-4o, which processes and generates outputs across text, audio, and image modalities in real time (Wikipedia, 2025).
Future systems will seamlessly create:
Articles with custom illustrations
Product descriptions with tailored images
Video narration synchronized with visuals
Interactive multimedia experiences
Real-Time Generation
Integrating NLG with real-time data streams (e.g., IoT sensors, stock markets) enables dynamic content creation that evolves with context (Macgence, 2025).
Applications include:
Live sports commentary
Real-time financial market analysis
Dynamic pricing descriptions
Emergency alerts
Personalized news feeds
Improved Reasoning
Some GPTs, such as OpenAI o3, spend more time analyzing the problem before generating an output, and are called reasoning models (Wikipedia, 2025).
Next-generation systems will:
Perform multi-step logical inference
Verify claims against knowledge bases
Explain reasoning chains
Handle complex analytical tasks
Reduce hallucination through deliberation
Domain-Specific Models
Rather than one-size-fits-all models, specialized systems optimized for specific domains:
Medical NLG trained on clinical literature
Legal NLG understanding case law
Financial NLG with accounting knowledge
Scientific NLG for research papers
Better Explainability
GPT-4 lacks transparency in its decision-making processes. If requested, the model is able to provide an explanation but these explanations are formed post-hoc; it's impossible to verify if those explanations truly reflect the actual process (Wikipedia, 2025).
Future systems will offer:
Transparent reasoning traces
Source attribution for facts
Confidence scores for statements
Audit trails for compliance
Editable intermediate representations
Collaborative AI
Moving beyond full automation to human-AI partnership:
AI generates drafts; humans polish
Humans provide sketches; AI expands
Interactive refinement loops
Style transfer learning from human edits
Personalized AI writing assistants
Major players in natural language generation are innovating by developing advanced technology through the integration of purpose-built stacks for AI-powered applications (Globe Newswire, 2024).
In November 2023, Microsoft's Azure announced Azure OpenAI integration, incorporating NLU and NLG capabilities powered by Azure OpenAI, providing competitive edge and superior performance for content summarization, image understanding, semantic search, and natural language to code translation (Globe Newswire, 2024).
FAQ
What is the difference between NLG and NLP?
Natural Language Processing (NLP) is the broad field encompassing all AI work with human language, including understanding and generation. Natural Language Generation (NLG) specifically focuses on the production of human language from data or other inputs. NLG is a subset of NLP alongside Natural Language Understanding (NLU), which handles comprehension.
How accurate is Natural Language Generation?
Accuracy varies dramatically by system type and application. Rule-based NLG systems achieve near-perfect factual accuracy when data is correct, as they cannot invent information. Neural systems produce highly fluent text but may hallucinate—studies show 23% of LLM outputs contain minor inaccuracies (Stanford, 2023). For regulated applications like financial reporting, human review remains essential.
Can NLG replace human writers?
NLG complements rather than replaces human writers. It excels at high-volume, data-driven, structured content but struggles with nuanced analysis, creative storytelling, and complex argumentation. The Associated Press increased earnings coverage 12-fold with NLG while redirecting journalists to investigative reporting. Most successful implementations use human-AI collaboration.
What industries benefit most from NLG?
Industries with high-volume, data-intensive reporting benefit most: financial services (earnings reports, portfolio summaries), healthcare (clinical documentation, patient summaries), e-commerce (product descriptions), journalism (sports, weather, financial news), customer service (automated responses), and business intelligence (analytics reports).
How much does NLG software cost?
Costs vary widely by approach. Template-based platforms like AX Semantics start around €899/month ($950) for subscription access. Enterprise solutions with custom integration range from $50,000 to $500,000+ for implementation. Cloud API services like OpenAI's GPT models charge per token (around $0.03 per 1,000 tokens). Internal development requires engineering resources and infrastructure.
Does Google penalize AI-generated content?
Google's guidelines target content generated programmatically to manipulate search rankings through keyword stuffing or spam. High-quality NLG content providing genuine value to users does not violate guidelines. Google evaluates content quality, not authorship method. The key is creating helpful, original, substantive content that serves user needs.
Can NLG work in multiple languages?
Yes, modern NLG systems support multilingual generation. Rule-based systems like AX Semantics support 110+ languages by translating templates and rules. Neural models trained on multilingual data can generate in dozens of languages, though quality varies. Translation quality depends on training data availability for each language.
How do I get started with NLG?
Start by identifying a specific, well-defined use case with structured data. Evaluate whether rule-based or neural approaches suit your needs. For exploration, try cloud APIs (OpenAI, Google, AWS) with pay-per-use pricing. For production, consider platforms like Automated Insights Wordsmith or AX Semantics. Pilot with a small project before scaling.
What data format does NLG need?
NLG systems work with structured data: databases, spreadsheets (CSV/Excel), JSON files, XML, or API responses. Data should include relevant attributes (product features, financial metrics, patient demographics) in consistent formats. The more structured and complete your data, the better the output quality.
How can I prevent NLG hallucination?
Mitigate hallucination through:
(1) using rule-based systems for factual content
(2) implementing retrieval-augmented generation to ground responses in verified sources
(3) adding human review for high-stakes content
(4) using conservative generation parameters
(5) integrating fact-checking modules
(6) maintaining knowledge graphs for verification
(7) establishing clear evaluation metrics.
Is NLG suitable for creative writing?
Neural NLG models can produce creative content including stories, poems, marketing copy, and fictional narratives. However, truly original creative work requiring deep human experience, cultural understanding, or artistic vision remains challenging. NLG works best for creative applications with structure (product marketing, templated narratives) or as a drafting tool for human refinement.
What's the ROI of implementing NLG?
ROI varies by use case but documented benefits include: 80% reduction in reporting time (Global Retail Chain), 67% improvement in documentation efficiency (healthcare), 12-fold increase in content volume (Associated Press), and elimination of freelance copywriting costs. Calculate ROI by comparing implementation costs against time saved, volume increase, quality improvement, and opportunity value of redirected human resources.
Key Takeaways
NLG transforms structured data into natural human language through AI systems that analyze, organize, and express information in readable text
The market shows explosive growth, expanding from $655M in 2023 to a projected $2.5B+ by 2030 at 21.8% CAGR
Two main approaches exist: rule-based systems offering control and accuracy, and neural systems delivering fluency and versatility
Real-world success proven across industries: Associated Press scaled earnings coverage 12-fold, e-commerce companies generate 33,000+ descriptions in months, healthcare improved documentation efficiency 67%
Major applications span finance, healthcare, e-commerce, journalism, and analytics, each addressing industry-specific content challenges
Challenges remain manageable: hallucination, bias, and context understanding require mitigation strategies but don't prevent successful deployment
Best practices emphasize data quality, human oversight, clear use cases, and phased implementation rather than big-bang launches
Future trends point toward multimodal generation, real-time capabilities, improved reasoning, and human-AI collaboration rather than full automation
Implementation success requires matching technology to use case, starting small, ensuring data readiness, and maintaining quality standards
ROI manifests through speed, scale, cost reduction, consistency, and employee redeployment to higher-value work
Actionable Next Steps
Identify Your Use Case: List 3-5 content creation tasks in your organization that are repetitive, data-driven, high-volume, or create bottlenecks. Evaluate which would benefit most from automation.
Audit Your Data: Assess whether you have structured, complete, accurate data to feed an NLG system. Document gaps and create a data improvement plan if needed.
Start Small with a Pilot: Choose one specific, low-risk use case for initial testing. Set clear success metrics. Learn before scaling.
Explore Available Tools: Research platforms matching your needs—Automated Insights Wordsmith for data-to-text, OpenAI API for neural generation, AX Semantics for e-commerce descriptions. Request demos.
Build Internal Expertise: Assign a project team including data engineers, domain experts, and content creators. Educate them on NLG capabilities and limitations.
Establish Quality Standards: Define what "good" output looks like. Create evaluation rubrics covering accuracy, fluency, relevance, and brand voice. Plan human review processes.
Calculate Expected ROI: Estimate time saved, cost reduction, volume increase, and opportunity value. Build a business case for investment.
Plan Your Integration: Map how NLG fits into existing workflows and systems. Identify technical requirements for data connections and content publishing.
Test and Iterate: Generate sample outputs. Review quality. Refine rules or training. Repeat until performance meets standards.
Monitor and Improve: After deployment, track metrics continuously. Gather user feedback. Update rules, retrain models, and expand use cases based on lessons learned.
Glossary
Attention Mechanism: A neural network technique that helps models focus on relevant parts of input when generating output, crucial to transformer architecture success.
BLEU Score: Bilingual Evaluation Understudy—a metric measuring similarity between machine-generated and human-reference translations, though limited for evaluating overall quality.
Content Determination: The first stage of NLG pipeline where systems decide which information from data should be included in generated text.
Data-to-Text: Rule-based NLG approach that transforms structured data into natural language narratives using predefined templates and logic.
Hallucination: When NLG systems generate plausible-sounding but factually incorrect or unsupported information not present in source data.
Large Language Model (LLM): Neural networks with billions of parameters trained on massive text corpora, capable of understanding and generating human-like text (examples: GPT-4, Claude, Gemini).
Lexicalization: NLG pipeline stage where systems select specific words and phrases to express concepts, considering audience, tone, and style.
Named Entity Recognition (NER): NLP technique identifying and classifying proper nouns and specific entities (people, places, organizations, dates) in text.
Natural Language Processing (NLP): Broad AI field encompassing all computational approaches to understanding, interpreting, and generating human language.
Natural Language Understanding (NLU): NLP subset focused on teaching machines to comprehend meaning, intent, and context from human language input.
Neural Network: Computing systems inspired by biological brain structure, using interconnected nodes to learn patterns from data.
Parameter: Adjustable values in neural networks that the model learns during training, determining how it processes and generates text.
Retrieval-Augmented Generation (RAG): Technique combining neural generation with information retrieval, grounding outputs in verified source documents to reduce hallucination.
Semantic: Relating to meaning in language, as opposed to syntax (structure) or lexical (vocabulary) aspects.
Template-Based Generation: NLG approach using fill-in-the-blank structures where predefined text templates receive variable insertions from data.
Token: Basic unit of text processing—can be a word, part of a word, or punctuation mark—that models use for input and generation.
Transformer: Neural network architecture introduced in 2017 using self-attention mechanisms, now foundational to modern NLG systems.
Sources & References
Grand View Research (2024). Natural Language Generation Market Size Report, 2030. Retrieved from: https://www.grandviewresearch.com/industry-analysis/natural-language-generation-market (Data: Market valued at $655.3M in 2023, projected 21.8% CAGR to 2030)
The Business Research Company (2025). Natural Language Generation (NLG) Market Report 2025. Retrieved from: https://www.thebusinessresearchcompany.com/report/natural-language-generation-nlg-global-market-report (Data: Market expected to reach $2.32B by 2029)
Verified Market Reports (February 2025). Natural Language Generation (NLG) Market Size Report, 2033. Retrieved from: https://www.verifiedmarketreports.com/product/natural-language-generation-nlg-market/ (Data: $1.1B in 2024, $4.5B by 2033, 17.5% CAGR)
Straits Research (2024). Natural Language Generation Market Size Report, 2032. Retrieved from: https://straitsresearch.com/report/natural-language-generation-market (Data: $1.2B in 2023, $12.4B by 2032, 29.4% CAGR)
Research and Markets (2024). Natural Language Generation (NLG) Global Analysis. Retrieved from: https://www.researchandmarkets.com/report/natural-language-generation (Data: $1.18B in 2024, $6.86B by 2034)
Globe Newswire (May 2024). Natural Language Generation (NLG) Global Analysis Report 2024. Retrieved from: https://www.globenewswire.com/news-release/2024/05/03/2875103/28124/en/ (Data: G2.com reported 87.8% of companies increased data investments)
Emerj Artificial Intelligence Research (2024). News Organization Leverages AI to Generate Automated Narratives. Retrieved from: https://emerj.com/ai-case-studies/news-organization-leverages-ai-generate-automated-narratives-big-data/ (Case Study: Associated Press and Automated Insights)
Wikipedia (August 2024). Automated Insights. Retrieved from: https://en.wikipedia.org/wiki/Automated_Insights (History: 300M pieces in 2013, 1B in 2014, 1.5B in 2016)
Automated Insights (2024). Customer Stories - Associated Press. Retrieved from: https://automatedinsights.com/customer-stories/associated-press/ (Case Study: AP increased coverage from 300 to 4,400 stories quarterly)
Marketing AI Institute (July 2022). How the AP Writes Thousands of Content Pieces in Seconds. Retrieved from: https://www.marketingaiinstitute.com/blog/how-the-associated-press-and-the-orlando-magic-write-thousands-of-content-pieces-in-seconds (Analysis: Academic studies show readers can't distinguish automated from human-written articles)
AX Semantics (February 2024). Auto Generate Product Descriptions Using NLG. Retrieved from: https://www.ax-semantics.com/en/blog/auto-generate-product-descriptions-using-nlg (Application: E-commerce content automation)
AX Semantics (February 2024). How Content Automation Solves E-Commerce's Biggest Pain Points. Retrieved from: https://www.ax-semantics.com/en/blog/ax-semantics-launches-globally-to-help-solve-one-of-e-commerces-biggest-pain-points (Launch: 500+ customers including Porsche, Adidas, MyTheresa)
AX Semantics (November 2024). What is Natural Language Generation. Retrieved from: https://en.ax-semantics.com/natural-language-generation-explained/ (Technical: Software supports 110 languages)
AX Semantics (2024). Automated Product Descriptions. Retrieved from: https://en.ax-semantics.com/automated-product-descriptions-online-shops/ (Case Study: AKKU SYS generated 33,000 descriptions in 2 months)
Cogent Infotech (2024). 14 Use Cases of NLG in Healthcare. Retrieved from: https://www.cogentinfo.com/resources/14-use-cases-of-nlg-in-healthcare (Data: Healthcare NLP market $2.2B in 2022, projected $7.2B by 2027, 27.1% CAGR)
Veritis (June 2025). Advanced Natural Language Processing in Healthcare Solutions. Retrieved from: https://www.veritis.com/blog/natural-language-processing-in-healthcare-a-game-changer-for-medical-data-analysis/ (Data: U.S. healthcare NLP $1.44B in 2024, projected $14.7B by 2034, 26% CAGR)
BMC Medical Informatics and Decision Making (November 2024). Natural Language Processing Data Services for Healthcare Providers. Retrieved from: https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02713-x (Case Study: UK NHS NLP service with 26,086 annotations spanning 556 SNOMED concepts)
PMC - PubMed Central (2024). The Growing Impact of Natural Language Processing in Healthcare. Retrieved from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11475376/ (Research: NLP improved ICU mortality prediction AUC from 0.831 to 0.922)
Macgence (March 2025). Natural Language Generation (NLG): How It Works, Benefits & Real-World Use Cases. Retrieved from: https://macgence.com/blog/natural-language-generation-nlg-the-future-of-ai-powered-text/ (Data: Stanford 2023 study found 23% of LLM texts contained inaccuracies; McKinsey 2024 reported 87% of regulated enterprises use human-in-loop; Forrester 65% of enterprises use NLG)
Wikipedia (September 2025). Generative Pre-trained Transformer. Retrieved from: https://en.wikipedia.org/wiki/Generative_pre-trained_transformer (Technical: Transformer architecture, GPT models history)
Wikipedia (2025). GPT-3. Retrieved from: https://en.wikipedia.org/wiki/GPT-3 (Technical: 175B parameters, 350GB storage, 2048 token context window)
Wikipedia (2025). GPT-4. Retrieved from: https://en.wikipedia.org/wiki/GPT-4 (Technical: GPT-4 released March 2023, GPT-4o May 2024 with multimodal capabilities)
IBM (August 2025). What is GPT (Generative Pre-trained Transformer)? Retrieved from: https://www.ibm.com/think/topics/gpt (Technical: Overview of GPT architecture and capabilities)
AWS (2025). What is GPT AI? Retrieved from: https://aws.amazon.com/what-is/gpt/ (Technical: Transformer architecture explanation, training methodology)
arXiv (July 2024). Survey of Hallucination in Natural Language Generation. Retrieved from: https://arxiv.org/abs/2202.03629 (Research: Comprehensive survey of hallucination in NLG systems)
Nature - npj Health Systems (February 2025). Current and Future State of Evaluation of LLMs for Medical Summarization. Retrieved from: https://www.nature.com/articles/s44401-024-00011-2 (Research: 72% of healthcare firms automated clinical documentation, 67% efficiency improvement)
ScienceDirect (December 2024). Knowledge Graphs, Large Language Models, and Hallucinations. Retrieved from: https://www.sciencedirect.com/science/article/pii/S1570826824000301 (Research: Knowledge graphs as mitigation for LLM hallucinations)
TechTarget (2024). Exploring 3 Types of Healthcare Natural Language Processing. Retrieved from: https://www.techtarget.com/healthtechanalytics/feature/Breaking-Down-3-Types-of-Healthcare-Natural-Language-Processing (Analysis: NLP, NLU, and NLG in healthcare applications)
ACL Anthology (November 2024). Leveraging Large Language Models for NLG Evaluation. Retrieved from: https://aclanthology.org/2024.emnlp-main.896/ (Research: LLM-based evaluation metrics for NLG)
PMC - PubMed Central (1997). Natural Language Generation in Health Care Communication. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC61265/ (Foundational: Early NLG applications in healthcare)
Linnk AI (2024). Semantic Hallucination Detection in NLG Models at SemEval-2024. Retrieved from: https://linnk.ai/insight/natural-language-processing/Semantic-Hallucination-Detection-in-NLG-Models-at-SemEval-2024-Task-6-gVzw-8L4/ (Research: 80.07% accuracy in hallucination detection)
AIMultiple (2024). Top 10 NLG Software of 2025. Retrieved from: https://research.aimultiple.com/nlg/ (Market Analysis: Leading NLG vendors and employee counts)
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