{"id":5791,"date":"2026-03-25T09:00:00","date_gmt":"2026-03-25T08:00:00","guid":{"rendered":"https:\/\/learnllm.nl\/?p=5791"},"modified":"2026-04-10T00:04:16","modified_gmt":"2026-04-09T22:04:16","slug":"what-is-ai-art","status":"publish","type":"post","link":"https:\/\/learnllm.nl\/en\/kennisbank\/ai-modellen\/wat-is-ai-art\/","title":{"rendered":"What is AI art and how does image generation with AI work?"},"content":{"rendered":"

AI art is visual art generated or supported by artificial intelligence. You describe in text what you want to see and an AI model converts that instruction into a unique image. The technology is used for social media content, marketing materials, illustrations and product visualisations.<\/p>\n

What exactly is AI art?<\/h2>\n

AI art is a collective term for images that are fully or partially generated by an AI system. The most common form is text-to-image: you write a description and the model produces an image that matches that description.<\/p>\n

This is fundamentally different from traditional image editing software, where you manipulate pixels yourself. With AI art, you describe the desired result and let the model make the visual translation. The quality of your description, the so-called prompt, largely determines the quality of the final result.<\/p>\n

AI art falls under the broader category of generative AI: systems that produce new content based on training data. If you want to understand how generative AI works and what other applications it has, our article on what generative AI is<\/a> that out.<\/p>\n

How does AI image generation work technically?<\/h2>\n

The technical workings of AI image generation can be understood without in-depth knowledge. The process always starts with the prompt: a text description that tells the model what to generate, in what style and with what mood.<\/p>\n

There are three main types of algorithms used for AI image generation. GANs (Generative Adversarial Networks) work through two neural networks competing against each other. A generator creates images from random noise, while a discriminator judges whether the result is convincing enough. Through this competitive process, the generated image improves step by step.<\/p>\n

Diffusion models are a newer approach and the basis of tools like Midjourney and DALL-E. They gradually transform random noise into a recognisable image through a series of steps. This usually produces more stable and accurate results than GANs.<\/p>\n

Transformers make up the third category. They break down the prompt into individual elements and understand how words relate to each other. As a result, the model knows that \u201cneon lights\u201d fits a cyberpunk aesthetic and translates that association into the visual output.<\/p>\n

The more specific your prompt, the better the result. \u201cA cat\u201d produces a generic image. \u201cA fluffy orange cat sitting on a windowsill at sunset, photorealistic\u201d produces an image that is immediately useful. The art of working with AI image generators is largely in learning how to formulate effective prompts. The same goes for text generation with tools such as ChatGPT<\/a>, where the same principles apply.<\/p>\n

Which AI art tools do professionals use?<\/h2>\n

Choosing an AI image generator depends on your work context and the type of images you need. The most commonly used tools at the moment are Midjourney, DALL-E, Adobe Firefly and ChatGPT's built-in image function.<\/p>\n

Midjourney is known for artistic and visually strong output. It works through a Discord environment and takes some getting used to, but produces high-quality images that work well for marketing and creative projects.<\/p>\n

DALL-E is OpenAI's image generator and can be accessed directly through ChatGPT. You describe what you want and the model generates the image within the interface you already use. This makes it low-threshold for professionals already working with ChatGPT.<\/p>\n

Adobe Firefly is aimed at business use and is integrated into Adobe products such as Photoshop and Express. A key advantage: Firefly is trained on licensed content, which makes the legal position of generated images clearer for commercial use.<\/p>\n

In practice, many professionals combine several tools: Midjourney for artistic visuals, Firefly for marketing materials and DALL-E for quick concept visualisations. If you want to understand what types of AI tools there are and how they differ from each other, our article on the different types of AI<\/a> a good overview.<\/p>\n

What are practical applications of AI art?<\/h2>\n

For professionals without a design background, AI image generation is an instant time saver. You don't have to search for stock photos, hire a designer for an initial concept or invest hours in image editing software. You describe what you need and adjust the generated image until it's right.<\/p>\n

Common applications include creating social media visuals for company pages, illustrations for presentations and reports, mockups for product concepts, visual brainstorming sessions in the early stages of a project and personalised marketing materials for different target groups.<\/p>\n

For marketers and content creators, this means you can produce unique images faster and cheaper than with traditional methods. The images have not been used before, setting you apart from competitors who use stock photos.<\/p>\n

Want to learn how to use AI effectively in your daily work, including image generation and prompt techniques? In the ChatGPT course from LearnLLM<\/a> You will learn step-by-step how to work with AI tools for your field.<\/p>\n

What is the impact of AI art on creative professions?<\/h2>\n

AI image generation is changing the field of work for designers, illustrators and photographers. The technology makes it possible to produce images in seconds that previously required hours of skilled work. This has implications for who is asked for certain jobs and at what rate.<\/p>\n

At the same time, new roles are emerging. Professionals who are good at formulating prompts and directing AI output become a link between clients and technology. Creative knowledge remains necessary to assess whether a generated image is good quality and fit for purpose.<\/p>\n

Practice shows that AI image generation speeds up but does not replace the creative process. A marketer who can visualise concepts themselves without a designer produces faster work, but still needs content knowledge about what makes a strong image in the context of a campaign.<\/p>\n

What are the legal and ethical considerations in AI art?<\/h2>\n

AI image generators have been trained on large amounts of existing images from the internet, including work by artists used without explicit permission. This raises questions about copyright, permission and what it means to create original work.<\/p>\n

For business use, the legal position of AI-generated images is not yet clearly established everywhere. Copyright on fully AI-generated images without human creative input is not clearly regulated in many countries. Adobe Firefly offers a clearer starting point here by training only on licensed content.<\/p>\n

In addition, AI models contain the biases of their training data. A model trained on predominantly Western images may underrepresent or stereotype people from other cultures. Being aware of these limitations is part of using the technology responsibly. You can read more about the risks of AI and how to weigh them in our article on the risks of AI<\/a>.<\/p>\n

What are the trends and future of AI art?<\/h2>\n

AI image generation is developing rapidly. Models are becoming more accurate, prompt interpretation is improving and integration with other creative tools is increasing. Where you can already add AI-generated elements to existing images in Photoshop, that integration will become deeper in the coming years.<\/p>\n

A clear trend is the emergence of multimodal models: systems that combine text, image and sound as input and output. This makes it possible to generate a video from a text description, or link a piece of music to a generated image.<\/p>\n

For professionals, the most relevant development is that AI image generation is becoming increasingly accessible as part of existing work tools. The threshold to get started is becoming lower, but the quality of the output remains dependent on user knowledge and judgement.<\/p>\n

Want to understand how AI tools like image generators compare to open source alternatives and closed systems? Our article on open source versus closed AI<\/a> helps you make that trade-off.<\/p>","protected":false},"excerpt":{"rendered":"

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