Key takeaways:
- Generative AI is a type of artificial intelligence that creates new content—like text, images, videos, and code—by learning patterns from existing data.
- You can use generative AI to support many types of work, including writing, brainstorming, research, design, and coding, across both personal and professional workflows.
- Generative AI is powered by different models and architectures, each suited to different tasks, which is why some tools excel at language while others focus on visuals or multimodal creation.
- While generative AI offers speed and creative support, it has limitations, including inaccuracies, bias, and privacy considerations that require responsible use.
Generative AI is opening up new ways for you to create, communicate, and get work done. What once required hours of drafting, revising, or starting from scratch can now begin with a simple prompt, helping you move from idea to execution faster and with more confidence.
As these tools become more widely available, they’re reshaping how people approach writing, creativity, and problem-solving. Whether you’re exploring new ideas, refining a message, or tackling complex tasks, generative AI can act as a powerful collaborator, expanding what you’re able to accomplish.
But what exactly is generative AI, and how does it help you work more efficiently and creatively?
In this guide, you’ll learn how generative AI works and how people use it in practice. We’ll explore real-world examples and popular tools, explain the models behind this technology, and highlight both its benefits and limitations.
Table of contents
- What is generative AI (gen AI)?
- Examples of generative AI in action
- How generative AI works
- Generative AI models: What powers gen AI
- Popular generative AI tools
- How generative AI is used across industries
- Benefits of generative AI
- Limitations and risks of generative AI
- What’s next for generative AI
- Generative AI FAQs
What is generative AI (gen AI)?
Generative AI is a branch of artificial intelligence (AI) that creates new content, such as text, images, music, code, or audio, based on what it has learned from large amounts of data.
Unlike earlier AI systems that mainly analyze or label information, generative AI can produce original responses when you give it a prompt. For example, it can help write a paragraph, suggest ideas, rewrite a sentence, or explain a concept in plain language.
Generative AI works by using large, flexible models that are trained on many different kinds of data. Because of this broad training, one model can handle many tasks instead of just one.
You may already use generative AI in everyday tools. For instance, Grammarly offers generative AI features that you can prompt anytime you use Grammarly, in any of the apps and sites you already use to work. By integrating generative AI within a writing-focused experience, Grammarly helps you with tasks like drafting, rewriting, brainstorming, and editing while keeping quality, context, and responsible AI use in focus.
Examples of generative AI in action
Generative AI can be used to create different types of content depending on how it’s trained and applied. These examples focus on the kinds of outputs generative AI can produce. Later, we’ll explore how different industries use these capabilities.
Text and content generation
One of the most common uses is creating written content. You can use generative AI to draft emails, articles, reports, essays, or social media posts, as well as to summarize long documents, translate text, answer questions, or brainstorm ideas. These capabilities are especially helpful when you need to get started quickly or refine existing writing.
Image and video generation
With image and video generation, generative AI can turn written descriptions into original visuals. This makes it easier to visualize ideas, design marketing materials, or create content for presentations and social media, without needing advanced design skills.
Code development
In software development, generative AI can help you write and understand code. Common uses include generating code snippets, fixing errors, explaining how code works, or translating code between programming languages—speeding up development and supporting learning.
Audio and music
Generative AI also extends to audio. You can use it to create music, sound effects, or spoken voice for videos, podcasts, or other media. These tools make it possible to produce audio content more efficiently while exploring different styles and formats.
How generative AI works
At a high level, generative AI works by learning patterns from large amounts of data and then using those patterns to create new content. While the underlying technology is complex, the process can be understood in a few key stages.
1. Data collection and training
Generative AI models are trained on massive datasets that may include text, images, code, audio, or video. During training, the model analyzes this data to learn patterns such as grammar, visual structure, and relationships between ideas.
This learning happens using neural networks, which are inspired by how the human brain processes information. As data moves through layers of interconnected “neurons,” the model gradually improves its understanding of how different pieces of information fit together.
Training often combines:
- Supervised learning, where the model learns from labeled examples
- Unsupervised learning, where it explores patterns and relationships on its own
Before training, a model can’t generate meaningful output. After training, it has a general foundation that allows it to respond when you give it a prompt.
2. Parameters and predictions
Inside a generative AI model are billions of adjustable values called “parameters.” These parameters determine how strongly different pieces of information influence the output.
As the model trains, it fine-tunes these parameters to improve accuracy. Larger models perform well because they can capture more subtle patterns, allowing them to respond flexibly to different requests.
3. Content generation and refinement
When you enter a prompt, the model predicts what should come next—piece by piece, based on probability and context. Because this process isn’t deterministic, the same prompt can produce slightly different results each time.
After training, models are further refined using feedback, often from people reviewing sample outputs. These feedback loops help improve quality, usefulness, and alignment with human expectations over time.
In short: Generative AI learns from data, predicts likely outcomes, and improves through feedback.
Generative AI models: What powers gen AI
Generative AI models are the systems that power generative AI tools and determine what those tools can produce.
These models are built on underlying architectures, which define how a system processes information and learns patterns. You can think of the architecture as the design blueprint and of the model as a trained instance built using that design. Because different architectures are optimized for different tasks, the architecture a model uses largely determines what kind of content it can generate.
You may see the terms “model” and “architecture” used interchangeably elsewhere; in this guide, we use “architecture” to describe the underlying design and “model” to describe a trained system built using that design.
Common types of generative AI models include:
- Language models (Transformers): These models generate and understand text and are commonly used for tasks like writing, summarization, translation, coding, and reasoning. Most modern language models, often referred to as large language models (LLMs), are built on transformer architectures.
- Image and video models (Diffusion models): Designed to create images and video from text prompts or other inputs, these models are best suited for visual generation and design tasks and commonly use diffusion architectures.
- Image generation models (GANs): Built using generative adversarial networks, these models are best suited for producing realistic images, enhancing visuals, and creating visual effects through a competitive training process.
- Creative and exploratory models (VAEs): These models generate new content by learning underlying structure and are best suited for creative exploration, style-based generation, and experimentation.
- Multimodal models (Combined architectures): These models can understand and generate more than one type of content, making them well suited for tasks that blend text, visuals, and other media, often by combining multiple architectures under the hood.
Popular generative AI tools
If models and architectures are the brains behind generative AI, tools are how you actually use it. Generative AI tools are software applications that apply trained models to help you create content, solve problems, and automate tasks—often through simple prompts or built-in workflows.
While many tools rely on similar models behind the scenes, what sets them apart is how they’re designed to support specific tasks, such as writing, design, coding, or media creation.
Some of the most popular generative AI tools include:
Text, writing, and general-purpose AI
- Grammarly: An AI writing assistant that uses generative AI to proactively help you draft, revise, and refine writing, with context-aware suggestions for clarity, tone, and correctness
- ChatGPT: OpenAI’s conversational AI tool for writing, analysis, brainstorming, coding, and answering questions across many topics
- Google Gemini: Google’s AI assistant, designed to support research, writing, and productivity tasks, with integration across Google’s products and services
- Claude: Anthropic’s AI assistant, focused on natural, helpful conversations, commonly used for writing, analysis, and coding assistance
Image and visual generation
- DALL–E: An image generation tool that creates visuals from text descriptions
- Midjourney: An image generation platform known for producing stylized and artistic visuals from prompts
- Stable Diffusion: An open-source image generation model that allows for customization and greater user control
Code and development
- GitHub Copilot: A coding assistant that helps developers write and understand code across multiple programming languages
Video and audio generation
- Runway: A tool for generating and editing video content using generative AI
- ElevenLabs: An audio generation platform for text-to-speech and voice narration
Each of these tools specializes in different types of content and workflows, even when they’re built on similar underlying models. Many offer both free and paid versions with varying capabilities. For a deeper look at how these tools compare and how to choose the right one for your needs, check out our guide to generative AI tools.
How generative AI is used across industries
Beyond individual tasks, organizations use generative AI to improve efficiency, decision-making, and content creation across industries. Depending on your role, you may already see—or use—generative AI in the following ways:
- Healthcare: If you work in healthcare, generative AI can help you save time and focus more on patient care. Tools can generate draft prescriptions based on diagnostic criteria, summarize appointment notes, support ER triage by analyzing symptoms before clinicians meet with patients, and detect patterns of insurance fraud in large datasets.
- Finance and banking: In finance and banking, generative AI is used to strengthen security and improve customer experiences. You might encounter it in systems that detect fraudulent transactions in real time, generate financial forecasts and reports, support personalized customer interactions through AI-powered chatbots, or inform data-driven marketing strategies based on product performance.
- Marketing: For marketing teams, generative AI can help you create and optimize content at scale. For example, marketers often generate multiple versions of landing pages for A/B testing, localize content for different markets, brainstorm campaign ideas based on performance data, produce images and graphics for social media, and refine AI-generated content to sound more natural and on-brand with tools like Grammarly’s AI humanizer.
- Software development: If you’re a developer, generative AI can support you throughout the development lifecycle. Tools can help you generate code snippets or functions, debug existing code, translate code between programming languages, explain complex logic in plain language, automate certain security checks, and generate technical documentation from codebases.
Benefits of generative AI
Generative AI offers several practical benefits that can improve how you work, create, and solve problems:
- Increased efficiency: Generative AI can automate time-consuming tasks like drafting emails, summarizing long documents, or generating reports. By handling routine or repetitive work, these tools free you up to focus on tasks that require human judgment, creativity, or strategic thinking.
- Faster development: Because many generative AI models are already trained on large datasets, organizations and individuals can experiment and build solutions more quickly. Instead of starting from scratch, you can adapt existing models to new use cases—speeding up development, testing, and iteration.
- Supports creative ideation: Generative AI can act as a creative partner by producing multiple drafts, ideas, or approaches to a problem. You can use these outputs as a starting point—refining, combining, or improving them—rather than replacing your own creativity.
- Adaptability: Generative AI models are often flexible enough to apply what they’ve learned from one task to another. For example, the same model might help you write professional emails, brainstorm marketing copy, and generate technical documentation, making it a versatile tool across different workflows.
Limitations and risks of generative AI
While generative AI is powerful, it also has important limitations that are essential to understand:
- Inaccuracies: Gen AI models can produce responses that sound confident but are factually incorrect or misleading. This behavior—often called “hallucination”—means you should always verify important information, especially for high-stakes decisions.
- Bias in outputs: Because generative AI learns from human-created data, it can reflect biases present in that data. This may result in outputs that reinforce stereotypes or unfair assumptions related to race, gender, or other characteristics.
- Copyright concerns: Gen AI models are trained on vast amounts of data that may include copyrighted material. In some cases, outputs may closely resemble existing works, raising legal and ethical questions, particularly for commercial use.
- Privacy and security risks: Prompts you share with AI tools may contain sensitive or proprietary information. Without proper safeguards, there’s a risk of data exposure or misuse, making it important to understand how a tool handles data and protects user privacy.
Understanding these limitations helps you use generative AI more responsibly, set realistic expectations, and make informed choices about when and how to rely on AI tools.
What’s next for generative AI
Generative AI is no longer just an experimental technology—it’s becoming an essential part of how we create, communicate, and solve problems. Whether it’s used for writing and design or software development and research, these tools are changing what’s possible at every stage of work.
As generative AI continues to evolve, staying informed and intentional will help you get the most value from these tools. By pairing AI’s capabilities with your expertise, you can navigate what’s next and make generative AI work for you—not the other way around.
The key to using generative AI effectively lies in understanding its strengths and limitations. When used thoughtfully, generative AI can enhance creativity, improve productivity, and help you work with greater confidence while still keeping human judgment, originality, and responsibility at the center.
Grammarly is one way to put this new technology into practice to enhance—not replace—the work you do while also prioritizing responsible AI use. Built directly into the tools you already use for work, Grammarly brings generative and agentic AI into your existing writing workflows—offering in-the-moment suggestions as you type. Grammarly proactively highlights where your work could be clearer, stronger, or more engaging and guides you through every stage of the process, from early ideas to polished drafts. By tailoring feedback to your context, audience, goals, and voice, you can communicate more effectively without breaking your flow or switching tools—so AI becomes a natural extension of how you already work.
Generative AI FAQs
What are generative AI examples?
Generative AI examples include tools that create new content, such as text, images, video, code, and audio, using AI models trained on large datasets to produce humanlike outputs.
- Text and content: Tools like Grammarly, ChatGPT, Google Gemini, and Claude help write and revise text, summarize documents, translate languages, and answer questions across personal and professional tasks.
- Images and video: DALL·E, Midjourney, and Stable Diffusion generate images from text prompts, while Runway and Sora help generate or edit video content for marketing, creative projects, and social media.
- Code development: Coding assistants like GitHub Copilot help write code snippets, debug errors, translate between programming languages, and explain how code works.
- Audio and music: Tools such as ElevenLabs generate realistic text-to-speech audio, while Suno and Udio create original music for podcasts, videos, and other creative projects.
What is the difference between AI and generative AI?
AI is a broad term for technologies that analyze data, recognize patterns, and make predictions or decisions—such as classifying images, recommending products, or detecting fraud.
Generative AI is a subset of AI that goes a step further by creating new content, including text, images, audio, or code, based on patterns learned from existing data.
In short, AI is used to analyze and predict outcomes, while generative AI is used to create new content.
What’s the difference between generative AI and agentic AI?
Generative AI creates content, such as text, images, or code, in response to your prompts.
Agentic AI goes a step further by using generative AI as one of its tools to work toward goals autonomously. It can plan steps, make decisions, and carry out multistep tasks with minimal human input.
For example, Generative AI can write an email when you ask. Agentic AI could schedule an entire meeting by checking calendars, finding available times, sending invitations, and confirming attendance, without you managing each step.
For a deeper side-by-side breakdown, check out our guide on generative AI versus agentic AI comparison.
What’s the difference between generative AI and machine learning?
Machine learning (ML) is a branch of AI focused on training models to learn from data and make predictions or decisions, such as classifying emails as spam, recommending products, or forecasting trends.
Generative AI is a specialized type of machine learning that goes a step further by creating new content, including text, images, audio, or code, based on patterns learned from data.
For example, a traditional ML model might predict whether an email is spam or estimate stock prices. A generative AI model, by contrast, can write an original article or generate an image in a specific artistic style.
Is ChatGPT generative AI?
Yes. ChatGPT is a generative AI tool that uses LLMs to create original text and other outputs in response to prompts.
Rather than simply retrieving or labeling existing information, ChatGPT generates new content by learning patterns from large datasets and predicting what comes next. This ability to produce humanlike text—such as explanations, summaries, or code—makes ChatGPT a well-known example of generative AI.
Is Grammarly generative AI?
Yes. Grammarly is an always-there AI writing partner using generative AI features, AI agents, and other AI technologies to help you draft, revise, adjust tone, reply to messages, brainstorm ideas, and more, all while keeping quality, user intent, and responsible AI use in focus.
Will generative AI replace human jobs?
Generative AI is designed to complement human capabilities by automating repetitive tasks and enhancing efficiency. For roles requiring creativity, critical thinking, and strategy, AI acts as a powerful tool rather than a replacement. The technology is reshaping certain roles and creating demand for new skills, but human judgment and expertise remain essential—especially for reviewing AI outputs, making strategic decisions, and performing tasks requiring deep understanding or ethical considerations. It’s more about collaboration than substitution.
