Glossary: AI jargon, in plain English.
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.
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.
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.
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.
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.
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.
Prompting and interaction
Chain of Thought
Chain of thought (CoT) is a prompting approach where the model shows intermediate reasoning steps before the final answer.
Context Assembly
Context assembly is how a product builds the full prompt stack before send: system rules, @-mentioned files, retrieved docs, memory, and the user’s message combined into one model request.
Few-Shot Prompting
Few-shot prompting includes a small number of example input/output pairs in the prompt so the model mimics your format, tone, or decision style.
Multimodal Input
Multimodal input lets users combine text with images, audio, video, or files in one request so the model can reason across media types.
Progressive Disclosure
Progressive disclosure reveals AI output in layers: summary first, details on expand, advanced controls only when needed.
Prompt
A prompt is the instruction or question you give an AI model: the user message, template, or form that tells it what to do.
Retrieval and model behavior
Context Window
The context window is the maximum amount of text (in tokens) a model can consider in one request: your prompt, system instructions, retrieved docs, and chat history combined.
Embeddings
Embeddings are numerical representations of meaning that let systems compare how similar two pieces of text (or images) are, even when wording differs.
Grounding (UX)
Grounding in UX is how an interface ties AI answers to verifiable sources, documents, URLs, files, or tool results. So users can check claims.
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) retrieves relevant documents or records first, then asks the model to answer using that material.
Semantic Search
Semantic search finds content by meaning and intent, not just exact keyword matches, powered by embeddings and vector comparison.
Vector Database
A vector database stores embeddings and retrieves the nearest matches quickly: the infrastructure behind semantic search and RAG at scale.
Safety and trust
Capability Disclosure
Capability disclosure is the practice of telling users. In plain language. What an AI feature can and cannot do before and during use.
Explainability
Explainability is how clearly an AI product shows why it produced an answer: sources, reasoning steps, confidence, or feature influence.
Guardrails
Guardrails are rules, filters, and policies that block unsafe inputs, limit risky outputs, and keep AI behavior aligned with product and brand standards.
Hallucination
A hallucination is when an AI states something confidently that is false, outdated, or unsupported by its inputs.
Human in the loop
Human-in-the-loop (HITL) means a person reviews, approves, or corrects AI output before it affects users, records, or systems.
Moderation
Moderation is the process of detecting and handling harmful, abusive, off-brand, or policy-violating content in AI inputs and outputs.
Agents and workflows
A2A (Agent-to-Agent)
A2A (agent-to-agent) communication is when one AI agent delegates tasks, shares context, or negotiates outcomes with another agent instead of only talking to the user.
Action Receipt
An action receipt is a plain-language record of what an agent did, when, and with what permissions, like a bank notification, not a server log.
Agent
An agent is an AI system that plans steps, uses tools, and acts across multiple turns to complete a goal, not just answer a question.
Agentic UX
Agentic UX is the design of interfaces for software that plans and acts on a user's behalf, tools, files, APIs, and multi-step workflows, not only text replies.
Autonomy Slider
An autonomy slider lets users set how independently an agent may act, from suggest-only to draft to execute, often per task or surface.
Background Agent
A background agent (or cloud agent) runs a task outside the active chat, on a remote VM, queued worker, or local process, while the user works elsewhere.
Output and formats
Agent Skill
An agent skill is packaged, reusable expertise an AI assistant loads for a specific job, accessibility audit, PRD draft, Figma handoff, without the user rewriting instructions each time.
AI-Native Design System
An AI-native design system encodes tokens, components, and rules that both designers and coding agents can consume, often via DESIGN.md, SKILL.md, or structured exports.
Artifact
In chat products, an artifact is a deliverable the AI produces outside the message stream, code, documents, diagrams, mini-apps. Users edit, export, and version independently.
Design.md
Design.md is a markdown file that captures design system rules, component usage, tokens, and UX standards for humans and AI agents building your product.
Generative UI (GenUI)
Generative UI (GenUI) is when AI produces live interface elements (forms, dashboards, cards, charts) from prompts or data, not just static text.
JSON
JSON (JavaScript Object Notation) is a lightweight text format for structured data: keys, values, arrays, and nested objects that machines parse reliably.
Product and performance
AI Evals (Evaluations)
AI evals are automated or human frameworks that measure model accuracy, bias, safety, and task performance before and after you ship.
Compute
Compute is the processing power (GPUs, TPUs, cloud instances) used to train models and run inference when users generate, classify, or embed content.
Fine-Tuning
Fine-tuning adapts a base model to your domain, tone, or task by training on curated examples, beyond what a system prompt alone can reliably enforce.
GEO (Generative Engine Optimization)
GEO (generative engine optimization) is the practice of shaping content and structure so AI answer engines (ChatGPT, Perplexity, Gemini, Claude) cite and summarize your product accurately.
Inference
Inference is running a trained model on new inputs to produce outputs: the live “prediction” step users experience as chat, classify, or generate.
Latency
Latency is the delay between a user action and a usable AI response: time to first token, time to complete answer, or time to finish an agent run.
Frequently asked questions
What is the AI UX glossary?
The AI UX glossary defines 92 practical terms for designers, PMs, and marketers building AI products (LLMs, RAG, agents, guardrails, latency, and more) in plain English with UX-focused examples.
Who is the glossary for?
Product designers, UX researchers, PMs, and marketers who need shared vocabulary for specs, critiques, and AI feature reviews without reading ML research papers.
How does the glossary relate to patterns and frameworks?
Glossary terms explain concepts; patterns show interface conventions with demos; frameworks organize territories like agentic UX or chat UX. Term pages link to related patterns, prompts, and frameworks when a concept maps to shipped UI.
How should I use glossary terms in product work?
Use definitions in PRDs and design reviews, link term pages in specs for alignment, and follow related patterns when a term implies interface requirements: citations for RAG, approval steps for agents, streaming affordances for latency.