The vocabulary of Artificial Intelligence has grown alongside its creative possibilities. Today, anyone working with images, video, audio, or text needs to speak the "language" of AI to harness its full potential.

Knowing what a prompt is, how embedding works, or what inpainting means is not just a technical detail: it's the difference between using a tool in a basic way or exploring it to its limits.

In this article, we have gathered 20 terms that every creative needs to know to work with AI consciously, effectively, and innovatively. Each definition comes with examples and contexts from the creative world, so you can see how to apply it in your daily life.

1. AI — Artificial Intelligence 🤖

Artificial Intelligence is the field of technology that develops systems capable of simulating human abilities, such as reasoning, learning, perception, and decision-making. These systems process large volumes of data, recognize patterns, and adapt their behavior based on new information, becoming more efficient over time. Present in virtual assistants, generative models, and recommendation algorithms, AI is the foundation of tools that today create images, videos, texts, and sounds.

Example in the creative market: a design studio uses AI to create automatic variations of logos, color palettes, and typefaces to present to the client, saving hours of initial creation.

2. ML — Machine Learning 📊

Machine Learning is an area of AI that enables systems to learn patterns and make decisions based on data, without explicit programming for each action. It works with a training dataset: the system analyzes examples, learns their relationships, and then applies this knowledge to predict outcomes or generate new solutions.

Example in the creative market: a platform analyzes previous campaigns and automatically suggests layout, text, and color combinations with a higher likelihood of engagement.

3. DL — Deep Learning 🧠

Deep Learning is a machine learning technique that uses deep neural networks to process information hierarchically, learning everything from simple patterns to complex concepts. This approach is essential in tasks such as image recognition, natural language processing, and audio and video synthesis, enabling highly realistic and contextual results in creative projects.

Example in the creative market: in a video project, Deep Learning can be used to automatically apply color correction and lighting adjustments in each scene.

4. Neural Networks 🕸️

Neural networks are models inspired by the human brain, composed of artificial