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's 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.

To understand how these models go from a blank slate to a useful tool, it helps to break the process into three stages.

Training

Every generative AI model starts with training, the phase where the model ingests a massive dataset and learns to recognize patterns. Technologically speaking, it's like that scene in A Clockwork Orange where the main character is forced to watch hours of film with his eyes clamped open (maybe a little less traumatizing, though).

A language model may be exposed to hundreds of billions to trillions of tokens drawn from the web, books, and code repositories; an image model could ingest hundreds of millions of image-caption pairs.

During this exposure therapy, the model learns the underlying relationships between concepts, like what words typically follow others, how computer code is structured, and how pixels combine in selfies. This produces a foundation model—a general-purpose base that understands the world broadly but isn't yet optimized for any specific task. Most of the AI tools you've heard of (ChatGPT, Claude, Gemini) are all built on top of foundation models.

Fine-tuning

Generative AI models are then subjected to additional training processes like fine-tuning, reinforcement learning, reinforcement learning from human feedback (RLHF), and newer alignment methods like direct preference optimization (DPO) that adapt foundation models to better perform specific tasks. Instruction tuning teaches the model to follow directions rather than just predict likely text continuations. These alignment techniques use human feedback—or increasingly, automated equivalents—to reward better outputs and flag worse ones. This is a big part of why modern AI assistants feel like conversation partners rather than glorified autocomplete.

If you want to learn more about the algorithms underlying modern generative AI, here are a few resources worth checking out:

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.

Generation

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.

Types of generative AI models

Generative AI is a family of architectures, each with a different approach to learning and creation. These are the four you'll encounter most often.

Transformers

Transformers are the architecture behind most modern language models, including GPT, Claude, and Gemini. Introduced in 2017, they work by letting every part of an input weigh the relevance of every other part, regardless of where it appears in the sequence. This mechanism (called self-attention) is what allows a model to understand that "it" in a long sentence refers to something mentioned six clauses earlier, or that a question about "Apple" is probably about the company, not the fruit.

That ability to process context across long sequences made transformers the heavyweight champ of language tasks. But they've since expanded well beyond text: most agentic AI systems are transformer-based LLMs with external tools attached, and autoregressive image models like GPT Image 2 apply the same transformer-based approach to visual generation rather than language.

Diffusion models

Diffusion models are how most image generators work (though autoregression is becoming more and more popular). 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 until something coherent emerges.

The name comes from the training process: the model learns by having noise progressively added ("diffused") into training images, then learning to reverse that process. At generation time, it runs that reversal from scratch—starting from pure noise and working toward an image that matches the prompt. Tools like Midjourney, FLUX, and Stable Diffusion use diffusion as their core technique, typically with additional layers on top for text rendering, image editing, and prompt accuracy.

Generative adversarial networks (GANs)

GANs pit two neural networks against each other in a cage match of the ages: a generator that tries to produce realistic outputs, and a discriminator that tries to catch it faking. The generator improves at fooling the discriminator; the discriminator improves at spotting fakes. This loop runs faster than a hamster in a wheel until the generated content becomes hard to distinguish from the real thing.

GANs were the dominant image-generation architecture before diffusion models took over. They're still used today for synthetic training data, certain video effects, and deepfakes.

Variational autoencoders (VAEs)

VAEs compress input data and then decode it back into output. The difference from a standard autoencoder is that instead of mapping each input to a single fixed point in that space, a VAE maps it to a probability distribution. Sampling from slightly different points in that distribution lets the model generate new variations rather than just reconstructions.

In practice, VAEs often work behind the scenes inside larger systems rather than as standalone generators. Many diffusion models run their refinement process in a VAE-compressed latent space rather than raw pixel space.

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.

An AI-generated image of a movie billboard

I used ChatGPT to create the dog-parrot, then to create a movie outline based on the image, and finally to combine it all into a poster.

What you can do with generative AI

An infographic showing generative AI models and what you can do with them.

Now that you understand what GenAI is, let's look at what each category can do, how people are using it, and which tools are worth knowing.

Generate and work with text

Large language models (LLMs) are the category most people encounter first. 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

Write and review code

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. AI coding tools like OpenAI's Codex and Claude Code are incredibly capable because of the models running under the hood.

Coding models are capable of:

Generating new code
Explaining and commenting on existing code
Finding and fixing bugs
Refactoring and optimizing code

Create images and video

Image generators have become the most visually obvious demonstration of what generative AI can do (after all, who doesn't want to see that dog-parrot masterpiece?). In practice, the best image generators are AI layers built on top of a core model and 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 2, Nano Banana 2, and Midjourney. For video, Google's Veo 3.1 is the current benchmark; OpenAI's Sora was shut down in early 2026 with no direct replacement.

Create audio and speech

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. The leading TTS providers in 2026 include OpenAI, ElevenLabs, and Google, all offering near-human voice quality with real-time streaming.

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

Take action with AI agents

AI agents are LLM-based systems capable of multi-step reasoning, using external tools, and taking actions to complete a goal without hand-holding at every step.

Where a standard LLM answers a question, an agent figures out which tools to use, executes a plan, checks its own work, and adjusts if something goes wrong. If you give an agent access to your email, calendar, and CRM, and it can do things like draft a follow-up, check whether a meeting is scheduled, and update a deal status, without you stringing those steps together manually.

Zapier is a great example of agentic AI. With Zapier MCP, you can connect to 9,000+ apps straight from ChatGPT, Claude, or other AI tools, and run multi-step workflows directly from the chat window.

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Benefits of generative AI

I hope no one's arguing that generative AI isn't helpful, but if you need to convince your boss...

Speed: A repetitive task that takes a human an hour (like summarizing a document or reviewing code) could take a gen AI model seconds. Multiply that across your team, and the time savings compound quickly.
Personalization: Generative AI can work alongside your CRM and other data sources to tailor content for customers and employees. This helps everyone feel like they're talking to a real person (or at least a more intelligent bot) rather than an AI drone. Especially when you connect your AI tools with MCP servers like Zapier MCP, you'll get a lot more context in your work.
Creativity: Gen AI lowers the barrier to technical and creative work. A product manager can generate a campaign idea, while a citizen developer can write a functional script. It doesn't replace a specialist, but it helps generalists make progress without one.
Availability: Barring an upcoming Great Tech Labor War of 2134, AI doesn't have business hours. So, consumers or employees who need AI help during off-hours don't have to wait until morning, while automated workflows can operate around the clock.

Challenges of generative AI

If generative AI seems too good to be true, that's because it is. While it's a supremely powerful technology, there are still several roadblocks you need to be aware of before you get something confidently wrong in a public channel.

Hallucinations: Gen AI models can confidently generate information that's completely wrong, such as made-up stats or conflated concepts. Anything a model produces should be verified before it travels anywhere that matters.
Biases: Models learn from their training data, and that data reflects the real world. A model trained on biased or uninformed sources will absorb patterns such as associating certain professions with specific genders, adopting a group's viewpoint like a political party or a ravenous NFL fandom (looking at you, Eagles fans), or defaulting to Western cultural contexts.
Data privacy: Generative AI models have access to prompts, records, and anything else you connect them to. That information needs to be secured, or else you risk unauthorized eyes perusing your deepest, darkest secrets. You can do that with a provider like Zapier, which offers a wealth of governance standards, app access controls, and AI Guardrails to keep your information secure even when using AI models.
Output quality: Mix together hallucinations and biases, and you get a sliding scale of output quality. Teams that use generative AI should implement occasional human-in-the-loop checks to ensure errors don't compound within their systems.

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 and route the AI's output wherever it needs to go—docs, spreadsheets, CRMs, project management tools, or internal dashboards. Or you can use Zapier MCP to take action directly from your chat window, with governed OAuth-managed authentication across all your apps.

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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Related reading:

What is generative AI?