AI UX Foundations Glossary
Core vocabulary for how AI systems learn, predict, and generate, so you can scope features and talk clearly with engineering.
17 terms
- Foundations
AGI (Artificial General Intelligence)
AGI (artificial general intelligence) is the idea of AI that matches or exceeds human ability across most cognitive tasks, not just one narrow skill like translation or image generation.
- Foundations
AI Product Design
AI product design is the practice of shaping how people understand, trust, and control AI-powered features—not only how they look.
- Foundations
AI UX
AI UX (AI user experience) is how people perceive and interact with AI features: streaming replies, tool pickers, citations, autonomy, memory, and recovery when something goes wrong.
- Foundations
Artificial Intelligence (AI)
Artificial intelligence (AI) is software that performs tasks that normally require human judgment, such as understanding language, recognizing patterns, making recommendations, or generating content.
- Foundations
ASI (Artificial Superintelligence)
ASI (artificial superintelligence) is hypothetical AI that surpasses the best human minds across virtually all domains, including scientific creativity and strategic planning.
- Foundations
Design Engineer
In AI product teams, a design engineer sits between design and code—prototyping AI flows in real stacks, defining motion and states agents must preserve, and reviewing generated UI for trust and craft.
- Foundations
Deterministic vs. Stochastic
Deterministic systems return the same output for the same input every time. Stochastic (probabilistic) systems, including most LLMs, sample from possible answers so results can vary run to run.
- Foundations
Diffusion
Diffusion models generate images (and sometimes video) by iteratively refining random noise into coherent visuals guided by a text prompt.
- Foundations
IDE (Integrated Development Environment)
An IDE is a workspace that combines code editing, file navigation, terminal, debugging, and often AI chat in one application—VS Code, Cursor, Xcode, and JetBrains are examples.
- Foundations
Large Language Model (LLM)
A large language model (LLM) is an AI model trained on vast text to predict and generate language, used in chat assistants, copilots, search answers, and agents.
- Foundations
Machine Experience (MX) Design
Machine experience (MX) design is designing products so both humans and AI systems can read structure, meaning, and relationships—not only pixels.
- Foundations
Machine Learning
Machine learning (ML) is a branch of AI where systems improve at a task by finding patterns in data instead of being explicitly programmed for every scenario.
- Foundations
Model
A model is the trained engine that turns inputs (text, images, context) into outputs (answers, labels, actions). ChatGPT, Claude, and Gemini are products built on large language models.
- Foundations
Multimodal
Multimodal AI can process and generate more than text: images, audio, video, and structured files in the same workflow.
- Foundations
Probabilistic UI
Probabilistic UI is interface design for software that can produce different outputs for the same input—LLMs, recommenders, generative media—not deterministic CRUD.
- Foundations
RLHF (Reinforcement Learning from Human Feedback)
RLHF aligns a model’s behavior with human preferences by training on rankings, ratings, or corrections from people, not just raw internet text.
- Foundations
Token
A token is a small chunk of text (often a word fragment) that models use to read and write language. Input and output size, cost, and speed are measured in tokens.