Using generative AI for game development.
From concept art to worldbuilding, generative AI is opening new possibilities in design and production. Learn how AI for game developers is reshaping creative workflows.
Generative AI in simple terms.
At its core, generative AI is about creating something new. Unlike traditional AI systems that analyze, rank, or retrieve existing information, generative AI models learn from patterns in training data to produce original content like text, images, audio, video, or even code.
Some ways to use generative AI include:
- Summarizing a long paragraph into a short, punchy email.
- Generating images from simple text prompts, like a “sunlit living room in mid-century style.”
- Adapting product descriptions into social media posts for a specific audience.
Generative AI models don’t just find what’s already out there — they synthesize new content from scratch. It’s a powerful shift that’s creating new possibilities across creative, communication, and productivity workflows.
The evolution of generative AI.
One of the first examples of generative AI was ELIZA, a simple rule-based chatbot developed in the 1960s that mimicked human conversation. It was an early step toward today’s LLM systems.
Generative AI technology advanced rapidly with the rise of neural networks, then made a major leap with transformer architectures, which now power the large language models used for text generation. In parallel, image generation evolved from early variational autoencoders (VAEs) and generative adversarial networks (GANs) to today’s diffusion models — enabling rich, high-quality visuals with just a prompt.
These technological leaps, combined with massive data sets and increased computing power, are what shaped the modern generative AI definition — systems that don’t just process information, but generate original content across formats.
How generative AI works.
Generative AI starts with an input, like a text prompt, and uses it to create new content. Large language models generate text by predicting words in sequence, while diffusion models and other neural networks can transform random noise into images, video, or audio that match the prompt. Earlier systems like generative adversarial networks (GANs) and variational autoencoders (VAEs) also helped pave the way. Whether you’re asking for copy, visuals, or sound, the process is about turning a simple instruction into something new and original. Learn more about how generative AI works.
What is generative AI trained on?
Generative AI models learn from large, varied datasets so they can recognize patterns and create new content. Training sources often include web text, licensed collections like Adobe Stock, image–caption pairs and alt text, open-source code, and audio or video transcripts. The range and quality of this data shape how accurate, creative, and unbiased the results can be which is why data diversity and responsible practices are vital to AI ethics.
Generative AI versus non-generative AI.
Artificial intelligence is exactly what it sounds like — machines mimicking human intelligence to perform tasks. Common examples are voice assistants like Siri and Alexa, and customer service chatbots. While non-generative AI is still useful for things like automation, generative AI is transforming creative industries. We’ve only just begun to experience how and where it will help us achieve results that would have either taken much longer or not been possible at all.
Different types of AI excel in different areas:
- Generative AI creates new content like text, images, video, audio, and code so you can ideate and iterate quickly. The benefits of generative AI include speed, versatility, and more control over creative direction.
- Predictive AI forecasts outcomes or classifies items, such as estimating product demand, identifying fraud, or tagging images.
- Conversational AI powers assistants and chatbots that let you ask questions or complete tasks through natural dialogue.
- Agentic AI strings together steps like searching, planning, acting, and refining to complete larger goals.
Learn more about generative AI versus other AI.
What makes generative AI unique.
Generative AI expands creative possibilities while giving users more control. From fast iteration to multimodal tools like Firefly AI, it enables new ways to generate, refine, and align content to personal or professional needs.
Key generative AI differentiators include:
Creative versatility.
Generate text, images, video, audio, or code then refine and remix them for different formats or channels.
Creative control.
Steer results with prompts, style guides, brand palettes, or reference images to keep content on-brief.
Rapid iteration.
Move quickly from draft to draft, exploring multiple ideas without slowing down production.
Multimodal capabilities.
Combine inputs such as text prompts with reference images or brand elements to produce assets that match the look, feel, and message you need.
Key applications of generative AI in the real world.
Generative AI is already changing how teams across industries work, helping them move from idea to first draft faster while freeing up more time for creativity and refinement. From marketing and design to entertainment and architecture, its applications span every creative medium.
Key generative AI differentiators include:
Text.
Draft copy, product descriptions, email sequences, and long-form content that can be tailored to different audiences.
Images.
Generate artwork, design assets, concept art, and mood boards sized and styled for specific channels.
Audio.
Translate audio into different languages, clean up recordings, clone voices, and generate sound effects for projects.
Video.
Create storyboards, build scenes, and translate video for localized markets.
3D.
Produce procedural textures, materials, and variations of models for use in design and production pipelines.
What does generative AI mean for creatives?
Generative AI is reshaping creative work by giving professionals new ways to explore, refine, and deliver ideas. Instead of replacing imagination, it helps accelerate the process and reduce repetitive tasks so more energy goes into craft and storytelling.
Benefits of generative AI include:
Rapid prototyping.
Generate multiple concepts in minutes, then test, refine, or combine them before committing resources.
Brainstorming and visualization.
Expand on early ideas, bring abstract concepts to life, and see variations faster than traditional workflows allow.
Streamlined production.
Automate repetitive or manual steps so more focus can stay on design, strategy, and creative direction.
Cost and time savings.
Cut down on rounds of revisions, reduce production costs, and accelerate delivery timelines.
Brand consistency.
Produce variations at scale while staying aligned with brand guidelines and creative standards.
Commercially-safe content.
Use models trained on licensed and responsibly sourced data for peace of mind in your professional work.
From AI for graphic designers to generative AI for architecture, AI technology is already helping creative teams work faster and smarter.
The future of generative AI.
The rapid pace of change in AI technology makes it clear the future holds even more transformative applications of generative AI. Expect higher-quality outputs from shorter prompts, deeper integration within creative apps, and faster iteration across every medium. Responsible AI will remain central, focusing on transparency, creator credit, and safety. New tools, like AI avatar generators, can quickly create avatars for training, onboarding, or social content, highlighting how quickly generative AI is evolving to support real-world creative needs.
Generative AI is here to stay, so start experimenting.
Generative AI isn’t a fad or passing trend — it’s a creative shift that’s already reshaping how work gets done across industries. The sooner you explore generative AI and build it into everyday workflows, the faster you’ll see the benefits. From saving time and costs to sparking new ideas, the advantages far outweigh the challenges.
Getting started is easy. Try an AI image generator to draft concepts or explore style variations, or experiment with an AI video generator to quickly bring storyboards and scenes to life. Tools like Firefly make it easy to test, learn, and build confidence so you can discover the value of generative AI firsthand.
Using generative AI for art.
Generative AI makes it possible to explore countless styles, from photorealism to abstract. Discover AI art use cases that can inspire your next project.
How to get started with generative AI.
With Firefly AI, you can explore a range of tools that let you experiment safely and creatively — whether you’re drafting copy, generating images, or localizing video. The key is to start small, try out different prompts, and refine your approach until you find what works best.
- Decide what you want to create with generative AI.
Begin with a clear goal. Are you drafting text, generating design assets, translating content, or producing variations for different audiences? Knowing what you want to achieve helps focus your prompts and ensures your output stays on-brief. - Explore different generative AI tools and features.
Firefly AI offers a growing set of capabilities, including text effects, AI image generation, editing, and localization. Take the time to explore which features align with your project, so you can choose the right tool for each task. - Experiment with text prompts and references.
Start simple, then layer in details like subject, style, lighting, aspect ratio, or tone. References, such as brand palettes, style guides, or example images, can help shape results. You can also use negative prompts to steer away from things you don’t want in the output. - Revise and remix your generative AI creations.
Don’t stop at the first draft. Iterate on the directions you like, test alternative prompts, and remix outputs into cohesive versions optimized for specific channels or audiences. The more you refine, the more value you’ll get from each idea.
Generative AI FAQs
A practical way to classify AI is by what it does:
- Predictive AI analyzes data to forecast outcomes, like estimating demand or detecting fraud.
- Generative AI creates new content such as text, images, video, audio, or code.
- Conversational AI powers chatbots and virtual assistants that interact with people in natural language.
- Agentic AI can plan and take action by stringing together multiple steps to reach a goal.
Generative AI is a type of artificial intelligence that can create new things, like writing text, generating images, making music, producing video, or even code. Instead of just sorting or analyzing existing data, it takes input like a text prompt or a reference image and uses that to generate original content.
Generative AI doesn’t come from a single inventor. It’s the result of decades of research in machine learning, with many teams contributing along the way. Early work included simple chat programs like ELIZA in the 1960s, followed by advances such as neural networks, generative adversarial networks (GANs), and diffusion models. Today’s tools are built on that foundation by researchers and companies around the world.
Artificial intelligence (AI) is a broad term for machines that mimic human intelligence to perform tasks, like recognizing speech, recommending products, or powering chatbots.
Generative AI is a specific type of AI designed to create new content. Instead of just analyzing data or following rules, it can generate text, images, video, audio, or code from inputs such as prompts or reference files.
Some examples of generative include text tools that can draft emails, blog posts, or marketing copy. Image generators that turn prompts into artwork, product photos, or design assets. Video tools for storyboarding or localizing content, and audio tools that translate speech or generate sound effects. There are even generative AI systems that build 3D models or write computer code. Popular examples include AI chatbots, AI image generators, and AI video generators.
A large language model (LLM) is a type of AI model specialized for text — it powers generative tasks like writing and summarizing. Generative AI includes LLMs plus models for images, audio, video, 3D, and code.
A prompt is the input that guides a model's output. Clear instructions, context, and examples help the model produce more relevant results. You can also use reference images, style guides, or constraints to steer outputs.
Firefly models are trained on a combination of licensed and curated data designed for commercial use, including Adobe Stock.
You may also like
What is AI art & how does it work?
Generative AI is quickly becoming a vital tool for artists. Read on to learn about what generative AI art is, how to create it, and how you can use it in your practice.
7 art styles for AI prompts
Surrealism, Cubism, Impressionism — find out how to create images in these and other art styles with generative AI.
How to write AI prompts for architecture
With the right text prompt and a little bit of know-how, architects can use generative AI to explore, ideate, and elevate their vision.
AI prompts for graphic designers
Learn how to write effective prompts for generative AI to get amazing results for graphic design.
