Generative AI, a subset of artificial intelligence, allows for creating new content, such as text, code, images, designs, and videos, by learning from and building on existing data.
| AI Text Generator | Primary focus | |
|---|---|---|
1. | AI-powered conversational assistance. | |
2. | Multimodal AI-powered intelligence. | |
3. | AI-powered chatbot builder. | |
Explore how generative AI can be used to generate content in the form of text via 4 use cases and 2 case studies of AI text generation..
AI Text Generation tools
| Software | User Rating* | # of employees** |
|---|---|---|
| ChatGPT | 4.5 | 127 |
| Google Gemini | 4.4 | 182,502 |
| Microsoft Azure Cognitive Services | 3.8 | 244,900 |
| BLOOM by Hugging Face | 4.9 | 170 |
| Jasper AI | 4.7 | 1 |
| Copy AI | 4.7 | |
| Writer | 4.6 | 1,032 |
* Based on data from B2B review platforms.
** Based on data from LinkedIn
Inclusion criteria: Only AI-based procurement software solutions with at least 20 reviews across B2B review platforms are considered.
Ranking: Products are ranked based on the number of reviews across B2B review platforms.
AI text generation tools create and provide ready-made templates to create high quality content like:
1. OpenAI GPT-4o
OpenAI provides an API that allows developers to integrate GPT-4 and GPT-4o into their applications. This API can be used for a wide range of AI text generation tasks, including chatbots, content creation, and summarization. OpenAI provides an easy-to-use interface called ChatGPT, built on its GPT models, that allows users to interact with the AI for various tasks. It enables users to interact with the AI and generate text directly, making it accessible for non-developers.
2. Google’s Gemini
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.
3. Microsoft Copilot Studio
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.
4. Hugging Face
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.
5. Jasper AI
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.
6. Copy.ai
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.
7. Writer
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.
Use Cases of AI-Generated Text
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:
1. Content Creation for Marketing
AI text generation tools can produce high-quality, SEO-optimized blog posts and articles at scale.
- Blog Posts and Articles: AI tools can generate structured blog posts and articles on a variety of topics, helping marketers scale their content production while maintaining quality.
- Social Media Content: AI can create engaging social media posts tailored to various platforms, enabling brands to maintain a consistent online presence.
- Email Campaigns: Automated generation of personalized email content, from promotional messages to newsletters, helps businesses engage their audiences more effectively.
2. Copywriting and Ad Creation
AI tools create compelling ad copy for various platforms, including Google Ads, Facebook, and LinkedIn, optimizing for conversions and engagement.
- Product Descriptions: AI can generate detailed, SEO-optimized product descriptions for e-commerce websites, reducing the workload for content teams.
- Ad Copy: AI-generated ad copy can be tailored for different audiences and platforms, optimizing for clicks and conversions.
3. Customer Support and Chatbots
AI-powered chatbots provide instant, accurate responses to customer inquiries, addressing various topics from FAQs to complex troubleshooting, thereby enhancing customer satisfaction.
- Automated Responses: AI-powered chatbots can manage routine customer questions, offer troubleshooting tips, and complete basic transactions, helping to speed up responses and enhance customer satisfaction.
- Personalized Assistance: AI can generate customized responses based on customer history and preferences, making interactions more tailored and human-like.
4. SEO Content Optimization
- Keyword-Rich Content: AI can generate content optimized for search engines by incorporating relevant keywords and adhering to best SEO practices.
- Meta Descriptions and Tags: Automated generation of meta descriptions and tags helps improve the discoverability of content online.
5. Personalized Communication
- Customer Outreach: AI can generate personalized messages for outreach campaigns, whether for sales, marketing, or customer service purposes, increasing engagement rates.
- Dynamic Content Generation: Websites and applications can use AI to generate dynamic, personalized content for users based on their behaviors and preferences.
6. Educational Content and Tutoring
- Customized Study Materials: AI can create personalized study guides, quizzes, and instructional content tailored to a student’s learning style and progress.
- Automated Tutoring: AI-powered tools can provide instant feedback, explanations, and even generate practice problems for students.
7. Summarization of Large Texts
- Document Summarization: AI can condense lengthy documents, reports, or articles into concise summaries, making it easier for users to quickly grasp key information.
- News Summaries: Media organizations utilize AI to generate summaries of news articles, enabling readers to stay informed without needing to consume entire articles.
8. Script and Story Generation
- Creative Writing: AI is used to generate scripts for movies, TV shows, and video games, or to develop plot ideas and character dialogues, providing inspiration or even entire drafts for writers.
- Interactive Stories: In gaming and interactive media, AI can generate dynamic storylines that adapt to player choices, creating more immersive experiences.
9. Legal Document Drafting
- Contract Generation: AI can draft contracts, agreements, and other legal documents based on predefined templates and input parameters, saving time for legal professionals.
- Case Law Summarization: AI tools can summarize case law and generate briefs, assisting lawyers in their research and preparation.
10. Academic Research and Writing
- Literature Reviews: AI can assist in generating literature reviews by identifying and summarizing relevant research papers.
- Research Proposals: AI tools can assist in drafting research proposals by generating structured content based on a given topic or hypothesis.
11. Creative Writing and Poetry
- Poem Generation: AI can generate poems with specific themes, structures, or styles, serving as a source of inspiration or collaboration for poets.
- Storytelling: Authors utilize AI to generate story ideas, develop characters, and even craft entire narratives, exploring new creative possibilities.
12. News and Report Generation
- Automated News Writing: AI can generate news articles, particularly for financial reports, sports events, and other data-driven stories, freeing up journalists to focus on more in-depth reporting.
- Business Reports: AI tools can generate business reports, financial summaries, and other corporate documents by analyzing data and presenting it in a clear, structured format.
13. Translation and Localization
- Automated Translation: AI-powered tools can translate text from one language to another, helping businesses and individuals communicate across language barriers.
- Localized Content: AI can generate content that is culturally and linguistically adapted for different regions, improving relevance and engagement in global markets.
14. Automated Code Generation
- Code Snippets: AI can generate code snippets or even entire functions based on natural language descriptions, aiding software development and reducing the time required to write code.
- Documentation: AI can automatically generate documentation for codebases, making it easier for developers to understand and maintain software projects.
15. Interactive Voice Assistants
- Conversational Responses: AI-generated text is used in voice assistants like Siri, Alexa, and Google Assistant to provide users with responses that sound natural and relevant.
- Task Automation: Voice assistants can automate tasks such as setting reminders, sending messages, or controlling smart home devices using AI-generated text.
Case Studies
Case Study 1: The Washington Post’s “Heliograf” AI System
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.1
Case Study 2: Alibaba’s AI-Powered Copywriting Tool
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.2
Case Study 3: Evaluating claims
Insurance companies evaluate long-written applications in their claims management process to decide whether a case is eligible for the insurance settlement process.
An insurance company had difficulty in processing all these materials, sharing responsibilities, speeding up decision-making processes, and seeking a solution for improving the claim settlement process.3
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.
Case Study 4: AP automated financial report generation
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.4
FAQ
What is AI text generation?
Text generation is a field that has been developing since the 1970s and is regarded as a subsection of NLP(Natural Language Processing).5 Developing deep learning models for text generation is an ongoing process in the field of NLP. 6 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.
What are AI text generation models?
Another approach to text generation is to use a template-based model. 7 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. 8
What are key components of AI text generator models?
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).
What are popular text generator models?
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.
Learn more about report automation tools.
External Links
- 1. Automated Insights - Stats Perform. Stats Perform
- 2. Automated Insights - Stats Perform. Stats Perform
- 3. 5 Practical Examples of NLP Use Cases. statworx GmbH
- 4. Automated Insights - Stats Perform. Stats Perform
- 5. Text Generation | SpringerLink. Springer US
- 6. ScienceDirect.
- 7. GPT and Template-Based Text Models in NLG.
- 8. GPT and Template-Based Text Models in NLG.
Comments
Your email address will not be published. All fields are required.