If you've tried ChatGPT, Microsoft Copilot, Nano Banana, Sora, Grok, or any other AI chatbot or image generator, you've used generative AI (also called GenAI). Over the past few years, huge developments in generative AI and computing power have taken these kinds of tools out of research labs and made them a practical part of everyday life.
You've almost definitely used generative AI, but let's dig a little deeper and add some more context.
Table of contents:
What is generative AI?
Generative AI (also called GenAI) is a type of artificial intelligence (AI) that can create new content—like text, computer code, images, video, and audio—based on patterns it has learned from the data it was "trained" on. Unlike traditional AI models, generative AI models don't classify or analyze inputs; instead, they respond to a prompt with computer-generated outputs.
To be honest, if you can keep that in mind, you've already got a solid grasp of what generative AI is and what it can do.
How does generative AI work?
Generative AI uses algorithms called neural networks that are modeled on the learning and decision-making processes of the human brain. These neural networks have to be "trained" on huge quantities of data in order to learn the underlying patterns and relationships between concepts—like what words typically follow other words, how computer code is structured, and how pixels combine in selfies.
When you give a model a prompt, it doesn't check its training data for an answer. Instead, it makes a prediction as to what output is most likely to come next. For example, large language models (LLMs) predict what word—or really, what word fragment called a "token"—should come next in the sequence until it generates a full response. If you give it the prompt, it was a dark and… there's a very good chance the next set of tokens is stormy night.
Of course, this massively simplifies the complexity of the algorithms at play. GenAI models don't just regurgitate their training data or the most common word combinations in the English language. They typically introduce a non-deterministic amount of randomness so that the same prompt won't always generate the same response, and they encode tokens in multi-dimensional vector space so that they can understand that MacBook, bottomed-jeans, pie, and by Charli xcx are all appropriate follow-ons to the word apple.
Generative AI models are also often subjected to additional training processes like fine-tuning, reinforcement learning, and reinforcement learning from human feedback (RLHF) that adapt general-purpose models to better perform specific tasks. For example, ChatGPT uses a version of GPT-5 that's optimized for conversation, while some image models are fine-tuned so as not to generate objectionable content.
If you want to learn more about the algorithms underlying modern generative AI, here are a few resources worth checking out:
Transformers are one of the most important advances in generative AI. The Illustrated Transformer is the most accessible overview I've found.
DeepLearning.AI is a membership site that has courses exploring all aspects of generative AI.
Google's Machine Learning Glossary and IBM's Think blog both offer great explanations of AI concepts. They're moderately technical, but if you want to dig deeper, they're handy resources.
Generative AI vs. machine learning
Artificial intelligence is a broad field, and generative AI is just one part of it. To understand where GenAI fits in, it helps to have an idea of the other types of AI models that are widely used. These models are often based on traditional machine learning (ML) techniques. These include:
Predictive and classification models. These models are trained to predict or classify things based on their training data. They're used in email spam filters, fraud detection systems, supply chain optimization, and countless other behind-the-scenes tasks.
Recommendation and ranking models. These models decide what to show you, and in what order. They don't generate new content, but they power things like the recommendation algorithms for Netflix, YouTube, and Spotify, suggested product boxes on Amazon and Walmart, and news feeds and social media timelines.
Computer vision models. These models allow computers to see and assess the real world. They're used in robotics and autonomous vehicles.
As you can see, while these models predict, identify, classify, group, and analyze, they don't actually generate new or novel content. A classification image model will tell you if a photo contains a dog or a parrot, but it won't draw a dog-parrot. Similarly, Netflix's recommendation algorithm suggests movies you might like from its database—it doesn't imagine movies that don't exist.
Generative AI models, on the other hand, are more than capable of drawing dog-parrots and creating fictitious movies.
The big generative AI models
Now that you understand what GenAI is, let's look at the major kinds of generative AI models, what they're useful for, and a few examples you should know.
Large language models
Large language models or LLMs are GenAI models that handle language. ChatGPT's launch in late 2022 marked the start of the current AI boom, and it demonstrated that language models were good enough for the real world.
LLMs take a text prompt and generate a text output, but that description underplays how powerful these AI models have become. A prompt can include detailed instructions on how the model should act, what rules it should follow, as well as documents and written references. The output can be thousands of words long and work through a chain of reasoning to solve more complex problems. They can also be given access to external tools like web search and make decisions on what to research.
By combining all these features in various ways, here are some of the ways LLMs are used:
Chatbots. While modern chatbots like ChatGPT and Claude combine multiple AI models, at their core, they rely on an LLM to understand what they're being asked to do, to make decisions, and to generate responses.
Customer support, lead generation, and sales. LLMs can be trained on your company data and used to help customers, find new leads, and drive sales.
Translate between languages. LLMs are significantly better at translating between languages than previous AI tools.
Text generation, editing, and research. LLMs built into apps like Google Docs and Word can generate rough drafts, edit your writing, or adjust its tone.
Text summarization. LLMs can pull actionable takeaways from long notes, emails, and similar text documents.
Examples include GPT-5, Google Gemini, and Claude.
Read more: The best LLMs
Coding models
Coding models are specialized LLMs that can write and edit computer code. I've put them in their own category because they're one of the breakout uses of GenAI. Models like OpenAI's Codex and Claude Code are incredibly capable when used within integrated development environments (IDEs) or used in a more hands-off manner through vibe coding apps.
Coding models are capable of:
Generating new code
Explaining and commenting on existing code
Finding and fixing bugs
Refactoring and optimizing code
Image and video models
Image and video models generate images and videos, respectively. While some newer models like GPT Image 1 are exploring different techniques, most of these models use a technique called diffusion to generate results.
The model is trained on a vast amount of visual content, so it learns concepts like "bulldog", "bounce", "blue," and "Banksy." When given a text prompt, it starts with a field of visual noise and refines it in a series of steps to create something that matches.
In practice, the best image generators are more sophisticated with multiple AI layers built on top of the core image model. They typically undergo additional training so that they can more accurately generate text, use image prompts, and edit existing images.
Video models are built on the same ideas, but they have to include time; they operate in a four-dimensional latent space so they can generate coherent motion across frames.
Some of the major image models are GPT Image 1, DALL·E (now discontinued), and Nano Banana. Two of the most important video models are Sora (OpenAI) and Veo (Google).
Read more: The best image generators and the best video generators
Audio models
Audio models generate sound by creating a new audio waveform based on what they learned from their training data. There are a couple of different kinds, and like all generative AI, they've improved dramatically over the last few years.
Text-to-speech models generate human-sounding spoken word audio. These are often integrated with chatbots but can also be used as a standalone system as part of a help line or telesales tool. Most major AI companies have a couple of text-to-speech models. For example, OpenAI has TTS-1 and GPT-4o mini TTS, while Google has Gemini 2.5 Flash TTS and Chirp 3.
Music models are perhaps even more exciting. These GenAI models can create a song from a text prompt. Some can even generate lyrics using an LLM and then "sing" them as part of the track. Suno and Udio are two of the biggest apps here, and both use their own proprietary models.
Read more: The best voice generators
Automate generative AI with Zapier
Generative AI is powerful on its own—but it's even more useful when it's connected to the rest of your work.
Zapier lets you plug generative AI models into your everyday workflows so they can actually do something with their outputs. Instead of manually copying text from ChatGPT into a doc, pasting summaries into Slack, or uploading images one by one, you can automate the whole process.
With Zapier, you can:
Trigger AI models based on real events, like a new form submission, support ticket, or calendar event.
Send prompts to models like GPT, Claude, or image generators automatically, with dynamic context pulled from your apps.
Route the AI's output wherever it needs to go—docs, spreadsheets, CRMs, project management tools, or internal dashboards.
Chain multiple steps together so AI-generated content is reviewed, edited, approved, and published without manual handoffs.
For example, you could automatically summarize customer calls and log the key points in your CRM, generate first-draft blog outlines from research notes, or create personalized follow-up emails based on sales conversations. The AI handles the creative and analytical work; Zapier handles the busywork around it.
The result is generative AI that's embedded into your systems, not siloed in a chat window. Instead of experimenting with GenAI in isolation, you can turn it into a reliable part of how your team works—saving time, reducing friction, and making sure AI outputs show up exactly where they're needed.
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