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Generative AI, a subset of artificial intelligence, enables the creation of new content, such as text, code, images, designs, and videos, by learning from and building on existing data.
Explore how generative AI can be used to generate content in the form of text via 17 use cases and 5 case studies of AI text generation.
Note: Products are sorted in alphabetical order.
In AI text generation, a variety of generation models from autoregressive transformers to retrieval-augmented and diffusion-based approaches play a central role.
OpenAI offers an API that enables developers to integrate GPT-4 and GPT-4o into their products. These models support a wide range of text generation tasks, including chatbots, content creation, and summarization.
For non-developers, OpenAI provides ChatGPT, an intuitive interface built on its GPT models. This makes advanced AI capabilities accessible to anyone, whether for drafting content, answering questions, or experimenting with conversational AI.
Google Gemini is an emerging AI model that combines natural language processing with advanced multimodal capabilities. It’s designed to generate high-quality text and integrate seamlessly with Google’s suite of tools.
Microsoft Copilot Studio is a low-code tool designed for businesses to create and customize AI-powered Copilots(chatbots and virtual assistants). It integrates Microsoft Copilot with Power Platform, allowing users to build, deploy, and manage AI assistants for customer service, internal support, and automation.
Hugging Face offers a wide array of pre-trained models and tools for text generation, including GPT, BERT, T5, and more. It is popular among developers for its flexibility and ease of use in deploying AI models. The tool also provides an Inference API, allowing users to quickly deploy and use text generation models without needing to manage the underlying infrastructure.
Jasper AI (formerly Jarvis AI) is a tool specifically designed for marketers and copywriters. It helps generate marketing copy, blog posts, and other types of content, with features for optimizing and customizing the output.
Furthermore, they offer collaboration and commercial rights to the produced content, making them useful for business processes. Please feel free to read our article on generative AI tools if you want to learn more about and compare these tools.
Copy AI focuses on helping businesses create marketing copy, product descriptions, and social media posts. It offers a user-friendly interface where users can input their requirements and generate content within minutes.
Writer is an AI-powered writing assistant designed specifically for businesses. It helps teams produce on-brand content consistently, offering suggestions that align with company guidelines.
Using AI text generation tools, businesses can save time, allocate employees’ time for creative projects, generate error-free texts, and streamline their processes.
There are a number of different ways that AI text generation tools can be used in business, such as:
AI text generation automates the production of blog posts, ad copy, newsletters, and social media captions. Businesses leverageLLMs to create SEO-friendly, engaging, and scalable content tailored to different audience segments.
AI tools create ad copy for various platforms, including Google Ads, Facebook, and LinkedIn, optimizing for conversions and engagement.
AI-powered chatbots provide instant, accurate responses to customer inquiries, addressing various topics from FAQs to complex troubleshooting, thereby enhancing customer satisfaction.
AI assists educators and students by generating lesson plans, quizzes, explanations, and feedback. It also provides personalized tutoring and language-learning support.
AI text generation assists with contract drafting, compliance reporting, and summarizing legal documents. It helps legal teams process vast amounts of text more efficiently.
AI helps researchers by generating summaries of academic papers, literature reviews, and grant proposals. It also assists in coding and structuring research outputs.
AI generates stories, scripts, video dialogue, and creative content for the entertainment and media industries.
News organizations use AI to generate real-time updates, earnings reports, sports summaries, and financial news. AI assists journalists by drafting stories that can be later refined.
AI generates financial reports, loan denial explanations, investment insights, and market forecasts. Banks and asset managers use AI to improve decision-making and transparency.
AI generates job descriptions, interview scripts, and candidate communication templates, streamlining recruiting workflows.
Savista’s marketing team needed to scale high-quality thought leadership content while maintaining the strict standards required in the healthcare industry.1 Key problems included:
Savista implemented Jasper AI to turn subject-matter expert insights, interviews, and existing materials into multi-channel marketing assets, including blogs, emails, and social posts.
By using Jasper’s brand voice and campaign tools, Savista could maintain consistent messaging across different executives and channels while quickly transforming core content into full marketing campaigns. This allowed the marketing team to standardize workflows and produce high-quality content more efficiently.
Using Jasper delivered measurable improvements:
The Washington Post developed an AI tool named “Heliograf” to enhance its content creation capabilities, particularly for covering large-scale, data-driven events like the 2016 Rio Olympics and the U.S. Presidential election.
The primary objective was to increase the newsroom’s capacity to produce timely and accurate reports without overburdening the human journalists, who were focused on more complex stories that required in-depth analysis.
Heliograf was engineered to generate concise news updates and articles by processing structured data, such as election results, sports scores, and other numerical information. This AI system was seamlessly integrated into the newsroom’s existing workflow, where human journalists could oversee the AI’s output, making refinements as necessary to ensure the quality of the content.
This approach allowed The Washington Post to efficiently cover a broader range of topics, especially those that might have been overlooked due to limited human resources.
The results were significant. During the Rio Olympics, Heliograf generated approximately 300 short news reports, enabling the newspaper to provide comprehensive coverage of various events. This not only increased the volume of content published but also allowed the editorial team to focus on more critical stories.
Additionally, during the U.S. Presidential election, Heliograf’s ability to quickly and accurately report on local election results enabled The Washington Post to cover more elections than ever before, enhancing their overall reporting and providing readers with timely updates on a broader scale.2
Alibaba, the global e-commerce giant, implemented an AI-powered copywriting tool to assist merchants on its platform in creating product descriptions, marketing copy, and other content needed for online listings.
The tool was introduced to address the massive volume of content that millions of sellers required to generate compelling copy to attract customers but often lacked the time or expertise to do so effectively.
The AI copywriting tool, which leverages natural language processing (NLP) and deep learning, can generate up to 20,000 lines of content per second. It was designed to understand the context and tone required for different products and markets, allowing it to produce relevant and engaging copy with minimal human input.
Sellers on Alibaba’s platform could use the tool to create product descriptions by simply inputting a few keywords or phrases, after which the AI would generate multiple variations of the content for them to choose from.
The introduction of this AI tool led to significant improvements in efficiency and content quality across Alibaba’s platform. Merchants reported that the tool helped them save considerable time, allowing them to focus more on their core business activities.
Additionally, the consistent quality of the AI-generated content contributed to better customer engagement and increased sales conversions. Alibaba’s AI-powered copywriting tool has since become an essential resource for sellers, showcasing the potential of AI in streamlining e-commerce operations and enhancing the customer experience.3
Insurance companies evaluate long-form applications in their claims management process to decide whether a case is eligible for the insurance settlement process.
An insurance company faced challenges in processing materials, sharing responsibilities, expediting decision-making, and improving the claim settlement process.4
A deep learning model called sequence-to-sequence architecture was implemented to resolve the problem. This is a neural network type commonly used for machine translation, answering questions, and summarizing text. As a result of the adoption of this model, summaries of applications are generated, which makes the decision-making process faster and prevents the waste of time.
Business reporters produce quarterly financial reports that require gathering the income statement, balance sheets, and cash flow statement of a company. Regularly preparing these reports is time-consuming, reducing the amount of time that can be allocated to writing creative journal articles.
In order to overcome this problem, Associated Press, which suffers from the same problem, adopted a language generation tool that converts the collected data into a coherent report, allowing for 15-times more financial reports to be generated.5
Text generation is a field that has been developing since the 1970s and is regarded as a subsection of NLP(Natural Language Processing).6 Developing deep learning models for text generation is an ongoing process in the field of NLP. 7 As an example, the researchers are training Generative adversarial networks (GANs), which are generative models that are composed of a generator and discriminator and used for generating synthetic outputs for text generation.
Another approach to text generation is to use a template-based model. 8 Unlike GPT-3, these models do not work independently, and intermediate steps require human intervention. It is possible, however, to produce more structured texts based on templates without requiring humans to edit and control them after they are generated. 9
One of the AI text generation models that can generate text is GPT (Generative Pre-trained Transformer), or generative pre-trained transformer. This language model, built by OpenAI and released in 2020, has different models, including GPT-3.
GPT-3 is a much larger model than its predecessor, with over 175 billion parameters. It is trained on a variety of data sources, including books, articles, and code repositories to generate realistic texts like human writers. It is possible to create summaries, answer questions, use as a grammar checker, learn new ideas and make translations through GPT-3.
Transformer Architecture:
The Transformer model is the foundation of most modern AI text generators. It uses self-attention mechanisms to weigh the importance of different words in a sentence, allowing the model to understand context better than previous models like RNNs (Recurrent Neural Networks) or LSTMs (Long Short-Term Memory networks).
Pretraining and Fine-Tuning:
AI text generation models are often pretrained on massive datasets containing billions of words from books, websites, articles, and more. This pretraining allows the model to learn general language patterns. Fine-tuning is then performed on smaller, task-specific datasets to specialize the model for particular applications, such as customer support, creative writing, or coding assistance.
Language Models (LMs):
Unidirectional Models: These generate text by predicting the next word in a sequence, considering only the preceding context (e.g., GPT series).
Bidirectional Models: These understand and generate text by considering both the preceding and succeeding context (e.g., BERT, though it’s more for understanding text rather than generating it).
Seq2Seq Models: These models are used for tasks that require generating an entire sequence of text from an input sequence, like translation or summarization (e.g., T5).
There are several popular AI Text Generation Models:
GPT (Generative Pretrained Transformer): Developed by OpenAI, GPT models are among the most well-known text generators. GPT-3, GPT-4, and others are capable of generating coherent, contextually relevant text across a wide range of topics.
T5 (Text-To-Text Transfer Transformer): Created by Google, T5 is a versatile model that converts all NLP tasks into a text-to-text format, making it highly adaptable for text generation, summarization, translation, and more.
BERT (Bidirectional Encoder Representations from Transformers): Although primarily used for understanding text, BERT has inspired models that can also generate text by leveraging its deep bidirectional understanding.
XLNet: Combines the strengths of autoregressive models (like GPT) and bidirectional models (like BERT) to generate text that considers context from all directions.
CTRL (Conditional Transformer Language Model): A model designed to generate text that follows specific stylistic or topical constraints, allowing for more controlled text generation.
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