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Use everyday language to create extraordinary results with generative AI.
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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.
How generative AI works at a glance.
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.
How does generative AI work behind the scenes?
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.
Why generative intelligence is so intelligent.
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.
How generative AI is powered.
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:
Training.
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.
Inference.
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.
Generative AI can use a lot of energy, and companies developing these tools are increasingly aware of the environmental cost. Efforts to improve efficiency and reduce the carbon footprint are under way, but there’s still progress to be made.
How generative AI is trained.
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.
How different types of generative AI models work.
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.
- Large Language Models (LLMs).
Trained on massive text datasets, LLMs, like ChatGPT or Claude, generate natural-sounding language by predicting the next word in a sequence. This makes them powerful for writing, answering questions, and creating human-sounding dialogue. - Diffusion Models.
Commonly used for images and video, diffusion models start with random noise and gradually refine it into a clear result that matches a prompt. This is the method that powers the Firefly AI image generator. - Generative Adversarial Networks (GANs).
GANs use two networks: a generator that creates outputs and a discriminator that evaluates them. By competing, they improve until the generated images look realistic. GANs were an early driver of AI art and deepfake creation. - Variational Autoencoders (VAEs).
VAEs compress data into a simpler representation and then reconstruct it, allowing them to generate variations that capture the “essence” of the original. They’re especially useful for blending styles or producing multiple versions. - Transformer-based Models.
Transformers are the underlying architecture behind many generative systems, including both LLMs and diffusion models. They use an “attention” mechanism to understand relationships across data, like words in a sentence or pixels in an image, so outputs stay accurate and contextually relevant.
Together, these model types make it possible for generative AI to handle everything from text to video, and to keep evolving into more capable, creative systems.
How generative AI works in the real world and its advantages.
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.
Corporations and generative AI.
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 and generative AI.
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.
Quality, bias and safety challenges with generative AI.
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 AI isn’t always right. Generative AI can produce results that sound convincing but aren’t factually accurate. Because generative AI works by predicting patterns in data rather than verifying facts, it may present information that looks polished but is incorrect. The best safeguard is still human oversight and validation using trusted sources.
- Bias can be anywhere. Understanding how generative AI works — learning from large datasets that reflect real-world inputs — explains how societal biases such as those around gender or race can appear in results. While developers work to identify and reduce bias through design and ongoing oversight, users can also play a role by recognizing blind spots, refining prompts, and sharing feedback.
- Intellectual property rights are an issue. Generative AI is sometimes trained on vast collections of creative work, which can lead to copyright concerns. Professional creators are rightfully cautious about how their content is used, and courts are still addressing the legal boundaries. Adobe trains Firefly on licensed and rights-cleared data and supports initiatives like the Content Authenticity Initiative (CAI) and the proposed “Do Not Train” tag, so creators can control how their work is used.
Keeping up with changes in how generative AI works.
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.
Embrace the future of design with Adobe Firefly generative AI.
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.
How generative AI works for architecture.
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.
How generative AI works for game developers.
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.
How does generative AI work from prompt to output?
Here’s how generative AI works behind the scenes when you provide a prompt using Adobe Firefly or another generative tool. Each step combines advanced machine learning with user-friendly controls to create new content from your inputs.
1. Input and conditioning.
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.
2. Encoding.
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.
3. Context understanding and alignment.
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.
4. Generation.
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.
5. Guidance and controls.
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.
6. Post-processing and export.
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.
FAQs about how generative AI works.
Generative artificial intelligence is a branch of AI that focuses on creating or producing new content, such as images, music, or text, through algorithms and machine learning models. Generative AI typically requires user input, like a text prompt, to generate a corresponding output, including text, image, music, or video.
The mechanism behind generative AI involves training models on large datasets of text, images, audio, or video so they can learn patterns and relationships. When you provide a prompt, the model uses that knowledge to generate new content, like predicting the next word in text, or refining noise into an image, depending on how the selected generative AI model works.
An example of generative AI is an AI image generator like Adobe Firefly, which can create new images from text prompts or reference photos. Other examples include chatbots that generate text, tools that compose music, or systems that produce video and 3D models.
Adobe Firefly uses advanced models, like diffusion models for images, to transform your text prompts or reference files into new content. How generative AI works in Firefly is it learns patterns from licensed and rights-cleared data, then applies that knowledge to generate images, text effects, and other creative assets that are commercially safe to use.
There are many practical applications of generative AI, including writing computer code, creating imagery and video, summarizing reports, analyzing sales data, and serving customers.
Artificial intelligence is broadly defined as machines mimicking human intelligence to perform tasks. How generative AI works is by creating or producing new content, such as images, music, or text, through algorithms and machine learning models. Generative AI typically requires user input to generate a corresponding output.
Potential key benefits of artificial intelligence include improved efficiency, greater productivity, and enhanced creativity. Limitations include inaccurate information, bias promotion, copyright infringement, and energy consumption.
Businesses and individuals can look for places in their workflows where they could benefit from additional efficiency, productivity, or creativity. Businesses integrating generative AI should review generated results for accuracy, bias, and copyright infringement, while allowing employees to drive concepting and strategy. Individuals can use generative AI tools for work and play while being careful with their personal information.