As the boundaries between design and artificial intelligence continue to blur, understanding the essential terminology from both fields becomes increasingly important. Whether you're a designer exploring AI tools or a tech professional learning design principles, this glossary offers a quick reference to the most commonly used terms in both areas.
This chapter is your go-to guide for clear and concise definitions that will help you communicate confidently, collaborate effectively, and stay updated in the fast-evolving world of design and AI.
A
Algorithm
A set of step-by-step instructions a computer follows to solve a problem or complete a task. In AI, algorithms process data and learn patterns for tasks like image recognition or natural language processing.
A/B testing
A method of comparing two versions of a design or feature to determine which one performs better with users. Frequently used in UX and marketing.
Artificial intelligence (AI)
The simulation of human intelligence in machines that are capable of learning, reasoning, and self-correction. Commonly used in tools for image generation, predictive design, chatbots, and personalization.
B
Bias (in AI)
Refers to systematic errors in AI outcomes due to biased training data. Designers must be aware of potential ethical concerns when using AI-generated outputs.
Brand identity
The visual and emotional elements that represent a brand, including logo, typography, color scheme, and voice. Consistency is key in both digital and print media.
C
Chatbot
An AI-powered tool that simulates conversation with users. Often used in customer support and increasingly in design workflows for ideation and feedback.
Computer vision
An AI field that enables machines to interpret and understand visual information from the world, such as images or videos. Useful in applications like facial recognition, automated tagging, or real-time editing.
D
Data set
A collection of data used to train or test an AI model. In design applications, these data sets may include images, user behaviors, or text inputs.
Design thinking
A human-centered approach to problem-solving that includes steps like empathizing, defining, ideating, prototyping, and testing. Widely used in UX/UI projects.
E
Ethical design
An approach to design that considers the social impact, privacy, inclusivity, and fairness of products. Particularly important when working with AI systems.
F
Fidelity (in prototyping)
Refers to how closely a prototype resembles the final product. Low-fidelity prototypes are simple sketches; high-fidelity ones are interactive and polished.
Feature extraction
A process in machine learning where key elements of data are identified and used for analysis. For example, AI may extract design features from a set of images to generate new visuals.
G
Generative design
A design method that uses algorithms and AI to generate multiple design solutions based on constraints and parameters set by the designer.
Grid system
A structure of intersecting horizontal and vertical lines that helps designers organize content. Commonly used in UI, web, and print layouts.
H
Human-centered design
A creative approach that starts with understanding users' needs and focuses on creating solutions tailored to them. It aligns closely with ethical and inclusive design practices.
I
Interface (UI)
Short for User Interface. Refers to the visual elements through which users interact with a digital product, such as buttons, menus, and icons.
Inclusive design
Designing products that are accessible and usable by people with diverse abilities, backgrounds, and experiences. Often overlaps with accessibility and ethical design.
L
Large language model (LLM)
An AI model trained on vast amounts of text data to generate human-like language. Examples include ChatGPT and other generative AI tools useful in content creation and UX writing.
Layout
The arrangement of elements on a page or screen. A good layout guides the user's eye and improves comprehension and usability.
M
Machine learning (ML)
A subset of AI that enables machines to learn from data without being explicitly programmed. In design, ML powers tools for automation, recommendations, and personalization.
Mockup
A static design of a product or page that represents its appearance but not its interactivity. Used to showcase the look and feel of a design before development.
N
Neural network
A computational model inspired by the human brain, used in deep learning. Neural networks are foundational to many AI applications in design, such as image generation and language models.
P
Prompt (in AI)
A text input or instruction given to an AI model to generate a response. Prompt engineering is the practice of crafting effective inputs to get desired outputs.
Prototype
An early version of a product used to test ideas and gather feedback. Can range from low-fidelity sketches to high-fidelity interactive versions.
R
Responsive design
A design approach that ensures content adapts to different screen sizes and devices. Crucial for providing a seamless experience across desktop, tablet, and mobile.
T
Typography
The art and technique of arranging type to make written content readable, engaging, and visually appealing. In digital design, typography affects brand perception and user experience.
Training data
Information used to teach an AI system. Poor-quality or biased training data can lead to inaccurate or unfair AI outcomes.
U
User experience (UX)
The overall experience a person has when interacting with a product or system. It includes usability, accessibility, and emotional satisfaction.
User interface (UI)
See Interface. While UX focuses on the overall experience, UI is concerned with the look and feel of the product interface.
V
Visual hierarchy
The arrangement of design elements to show their order of importance. Used to guide users' attention and improve comprehension.
W
Wireframe
A basic layout or blueprint of a digital product that shows the structure without visual design elements. Used in early stages of UI/UX design to map out content and navigation.
Y
Your AI co-designer
A term used to describe the growing role of AI tools in the creative process. These systems assist rather than replace human designers, offering new ways to generate ideas, test variations, and speed up workflows.
This glossary will continue to evolve as technology progresses. Bookmark it as a reference, and don’t hesitate to revisit it as you dive deeper into the design-AI landscape.
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