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How does generative AI work behind the scenes?
Why generative intelligence is so intelligent.
How different types of generative AI models works.
How generative AI works in the real world and its advantages.
Quality, bias and safety challenges with generative AI.
Embrace the future of design with Adobe Firefly generative AI.
In the past year, generative artificial intelligence has captured the world’s imagination. This powerful type of AI can create new content — like images, music, writing, or code — based on patterns it learns from existing data. Generative AI works by using your inputs, such as a text prompt or a reference image, and then applying advanced models to produce entirely new outputs that match your request. That’s why it can make fantastical images, write poetry, generate software code, or even produce a song that sounds genuine.
Soon, generative AI may be as central to our lives as the smartphone. Yet to many, it remains a mystery. This guide looks at what generative AI is, what it’s not, and how it may change the way we work and create.
While generative AI might seem like magic, it’s powered by complex technology that learns from data and applies patterns to create something new. By breaking it down piece by piece, the “magic” becomes easier to understand.
Generative AI is artificial intelligence that doesn’t just analyze existing information — it generates brand-new content. Models are trained on massive datasets of text, images, audio, or video, where they learn patterns, relationships, and styles. When you give the model an input like a text prompt or reference image, it applies what it has learned to produce an original output that matches your request.
That’s why you can ask a chatbot to suggest a slogan and get a fresh idea in seconds, or use Firefly to transform a description into an image that looks hand-drawn or photorealistic. Beyond creative tasks, generative AI is being used in science and healthcare to design new proteins, improve cancer treatments, and accelerate research. Its potential goes far beyond wordplay — it’s already reshaping industries.
In the past, computer applications couldn’t perform a task unless humans first provided explicit instructions on how to accomplish the task. Those instructions are called “programming.” Although sophisticated programming can yield impressive results, a traditional computer application can’t do something humans didn’t include in its programming.
Generative AI systems are more flexible because they rely on machine learning, which doesn’t require explicit programming. Instead, humans give computers access to large amounts of data. The machines train themselves to recognize patterns in that data and, most importantly, to draw conclusions from what they’ve learned. (That’s where the learning part of “machine learning” comes in.) The size and quality of the dataset is important. AI is only as good as the data on which it’s trained.
Answering the question “How does generative AI work?” is complex, and gaining a deep understanding of it requires effort. The beauty of generative AI, however, is that you don’t need to understand everything about it to benefit from it. You can simply find an app, such as Firefly, type in what you want to see — “three labradoodle puppies run on the grass” — and presto, you’re now a generative AI user. No programming degree required.
Behind the scenes, generative AI depends on powerful hardware and large-scale computing to function. Graphics processing units (GPUs) and tensor processing units (TPUs) handle the massive calculations needed to train and run these models.
The process has two main phases:
During training, models learn from huge datasets of text, images, audio, or video. This stage is energy-intensive because it requires distributed computing, parallel processing, and long runtimes to recognize patterns and relationships.
Once trained, a model can generate outputs on demand like writing text, producing an image, or translating audio using much less energy. Inference can also be optimized through techniques like batching and deployment in the cloud.
To understand how generative AI works, it helps to look at what happens before you ever type a prompt. The AI training process includes careful data cleaning and curation to improve quality, pretraining on large datasets to establish a base of knowledge, and fine-tuning for specific tasks or domains.
Human feedback and safety tuning are also important, helping refine outputs and reduce unwanted bias. At Adobe, training incorporates licensed and rights-cleared data, including content from Adobe Stock, so professional creatives can use generative tools with confidence.
There are several kinds of generative AI models, and each works in slightly different ways. Understanding generative AI versus other AI can help you understand which model type is best suited for your specific project.
AI brought us virtual assistants. Generative AI, on the other hand, provides virtual experts — though with some limitations we’ll cover later. So how does generative AI work in the real world for businesses, professionals, and everyday users? By creating new content on demand, it’s helping teams move faster, explore more ideas, and deliver results that once required far more time and resources. The benefits of generative AI range from greater efficiency to expanded creativity, making it a valuable tool across industries.
Generative AI can add value by optimizing how organizations handle internal knowledge. Creative professionals can use tools like the Adobe Firefly AI character generator to develop unique characters for games, films, and marketing campaigns. Generative AI has the potential to allow, say, clothing chain strategists to search their company’s inventory records by asking questions like, “Did we sell more shorts or pants last summer?” Such insights could speed up decision-making and strategy development.
Beyond these examples, generative AI works across industries to boost productivity, improve efficiency, and spark creativity. It can analyze complex datasets from spreadsheets and reports to images and charts much faster than people can, helping teams find insights and make recommendations. For marketers, it can streamline repetitive tasks like resizing ads or reporting on asset performance. Creatives like graphic designers can use AI as brainstorming partners, suggesting fresh directions and variations that inspire new ideas.
Individuals already use generative AI to answer general questions and conduct research. (Note that the answers and research require human fact-checking — more on these and other drawbacks in the “Limitations and challenges of generative AI” section below.)
Making art with generative AI is also popular with individuals. You can quickly test concepts, build a mood board, and dream up extraordinary scenes from everyday language. There can be problems here, too, because many AI art generator tools are trained on copyrighted imagery.
However, there can also be concerns around copyright. To help address these concerns, Adobe trained Firefly on licensed images in Adobe Stock along with openly licensed content and public domain content where the copyright has expired. Since Firefly is designed to be used commercially, it can open doors to many other areas, such as commercial art, design, gaming, virtual environments, and more.
The capabilities of generative AI are astonishing, but it’s important to understand its limitations. These challenges, like accuracy, bias, intellectual property, and evolving rules around AI ethics, all stem from how generative AI works behind the scenes.
The rules, policies, and regulations around generative AI are still evolving. Businesses and individuals need to stay informed, review privacy policies carefully, and avoid uploading confidential information they want to keep private. For companies, this means reviewing outputs for accuracy, bias, and copyright concerns. For individuals, it means treating generative AI as a creative partner, not a replacement for human judgment.
Generative AI is already changing our lives. As a virtual expert, generative AI may improve efficiency and productivity in many industries. As a brainstorming partner, generative AI can enhance our creativity.
The technology behind how generative AI works is evolving so quickly that tomorrow’s generative AI may look very different than today’s. If we explore the tools with curiosity and caution, we can enjoy their benefits — and avoid any pitfalls.
Learn how generative AI is reshaping workflows in architecture.From early sketches to full 3D models, gen AI is helping architects explore concepts, refine designs, and visualize spaces faster than ever. Learn more about how generative AI works for architecture.
Discover how generative AI can speed up worldbuilding, character design, and asset creation — freeing game developers to focus more on storytelling and gameplay. Learn more about how generative AI works for game developers.
Start by entering a text prompt or uploading a reference image. The system conditions on these inputs, meaning it interprets what you’ve asked for and prepares to generate a result.
The input is converted into a numerical representation that the model can understand. For example, words are broken down into tokens, while images are transformed into data points describing shapes, colors, and features.
The generative AI model evaluates your input against what it has learned from training data, paying attention to relationships and context. This alignment helps ensure the output matches your intent and stays relevant to your request.
Using its training, the model generates new content, like predicting the next word in a sentence, refining random noise into an image, or producing audio that fits the description.
User settings, like style, aspect ratio, or brand palettes, guide the process. These controls help steer the output toward a specific look, tone, or use case.
The system polishes the output, improving quality and applying final adjustments. You can then download, export, or refine the result further with your favorite Firefly tools or Adobe apps.