AAcronyms and insider jargon have a way of splitting a room: either you're in the club or you're quietly nodding along. I have a low tolerance for acronyms, and this happens to be one of the few things Elon Musk and I see eye to eye on. He sent out a memo banning unnecessary acronyms and "nonsense words" at Tesla and SpaceX, on the grounds that people shouldn't need to memorise a glossary before they can do their job.
Anyway.
Below is a list of common AI terminology, explained in plain English with practical examples. If you work in design or product, you don't need the math. But knowing what these words mean is the difference between taking part in the conversation and just following along.
AGI (Artificial General Intelligence)
A hypothetical future AI that matches human capability across any task: not narrowly good at writing or image recognition, but flexible and self-directed. We're nowhere near it, and experts can't agree on when, or if, we'll get there.
Example: Today's AI is exceptional at some things and hopeless at others. AGI would pick up a new skill, move through the physical world, reason when information is incomplete, and make ethical calls. Everything a person can do.
Agent / Agentic workflow
An agent doesn't just answer. It takes a sequence of actions to finish a job for you. An agentic workflow is what happens when you hand over an entire task and the AI works out the steps, picks its tools, checks its own results, and keeps going until it's done.
Example: You ask an AI to "plan my team's quarterly offsite." Rather than handing you a checklist, it looks at your calendar, researches venues, drafts an agenda, and sends the invites, with no further input from you.
Agent skills
Reusable, self-contained abilities you plug into an agent so it can handle particular jobs, a bit like apps on a phone. They can be combined to build more capable agents.
Example: Claude has a design skill that reads your Figma files, picks up component names and styles, and generates code matching your existing design system instead of inventing components from nothing.
AI assistant
A product built to help you get things done through conversation: answering questions, drafting content, managing tasks. The "assistant" label usually signals that it waits for instructions rather than acting on its own.
Example: Claude, ChatGPT, and Gemini are assistants. You ask, they answer. An agent, by contrast, might take action before you've asked.
AI experience design (AIX)
The practice of designing how people experience AI products. Not only the UI, but how the system communicates uncertainty, handles failure, earns trust, and sets realistic expectations.
Example: Good AIX might mean a confidence indicator on outputs, an explanation of why a recommendation was made, or a graceful response when the AI simply doesn't know.
AI slop
Generic, low-effort content that's obviously machine-generated with no human thought or editing behind it. Technically fine, but hollow, repetitive, and impersonal.
Example: A blog where every article opens with "In today's fast-paced digital world..." and says nothing new, each one produced in 30 seconds and never touched by a human.
Anthropomorphism
Treating AI as though it has feelings, opinions, or intentions. It's an instinctive response, especially when a product is designed to sound warm, but it can push users toward overtrust or unhealthy attachment.
Example: Assistants are often built to be friendly, use your name, and respond with empathy. That's good UX. But some people start to feel real companionship, share personal struggles, and feel genuinely stung when the AI "doesn't care." Designers are increasingly on the hook for that dynamic.
API (Application Programming Interface)
The invisible pipe connecting two pieces of software. When a product "uses AI," it's almost always calling an AI company's API, sending a request and getting a response. You pay per use, which turns cost into a genuine design constraint.
Example: Type into a support chatbot and your message goes to OpenAI's or Anthropic's API, comes back with an answer, and appears on screen, all within seconds.
Claude Code
Anthropic's coding tool that lives in your terminal or editor. It writes, edits, explains, and debugs code, runs commands, and manages entire projects. Closer to a junior developer than an autocomplete.
Example: A designer describes a component in plain English; Claude Code writes the React component, creates the file, and drops it in the right folder. No manual coding.
Context window
How much text an AI can hold in view during a single conversation. Think short-term memory: anything that falls outside the window is gone.
Example: Paste a 200-page report into a model with a small context window and it might only take in the first 50 pages. A bigger window means the whole document stays in mind.
Copilot
A design pattern (and Microsoft's brand name) for AI that works next to you rather than instead of you, suggesting, assisting, speeding you up, while you keep the final say.
Example: GitHub Copilot proposes the next lines as a developer types. Microsoft Copilot drafts a paragraph in Word when you're stuck. Accept, reject, or edit. The decision stays yours.
Eval (Evaluation)
A test that checks whether your AI is performing well. Evals are how teams measure accuracy, tone, safety, and helpfulness before and after changing a model or prompt. A typical eval has four parts: test inputs, the AI's responses, expected answers to compare against, and a scoring method.
Example: Before shipping a new version of your support bot, you run it against 500 real past conversations to see whether it beats the old one, scoring accuracy, tone, and how often it stayed away from hallucinating.
Feedback loop
The system that lets users flag whether a response was good or bad, and feeds that signal back into improving the model. Thumbs up/down is the simplest version.
Example: Every 👍 or 👎 on a ChatGPT response feeds the loop. Across millions of interactions, those signals teach the model what people actually find useful.
GenAI (Generative AI)
AI that produces new content (text, images, video, audio, code) instead of only analysing or classifying what already exists. It's the category covering ChatGPT, Midjourney, and Claude. When people say "AI" in a product or design context today, this is nearly always what they mean.
Example: You type a brief into Midjourney and get a mood board image that didn't exist a moment ago. You describe a user flow to Claude and it writes the copy for every screen. Neither is retrieving something. Both are making something.
Gen UI (Generative UI)
When AI builds the interface itself (buttons, maps, cards, layouts) on the fly, based on what you asked, rather than serving a screen designed in advance. The interface forms around the content.
Example: Ask "where's a good coffee shop near me?" and instead of text you get a live map with pins. Ask for hotels in Lisbon and instead of a list you get cards with photos, prices, and ratings, assembled in the moment for that specific question.
Guardrails
The rules and limits built into an AI product to keep it from going off-script: dangerous advice, off-brand tone, embarrassing statements. The product's code of conduct.
Example: A children's education app sets guardrails so the AI never touches violence or adult topics, stays encouraging, and steers off-topic conversations back to learning.
Hallucination
When AI states something false with total confidence: a made-up statistic, a person who doesn't exist, a wrong date. It isn't lying. It's pattern-matching into convincing nonsense.
Example: You ask for sources to back a claim and get three plausible academic papers that were never written, complete with invented authors and journals.
Human-in-the-loop (HITL) / Supervisor
A pattern where a person reviews or approves an AI action before it happens. Essential for high-stakes moments where a mistake would be costly or humiliating.
Example: An AI drafts a refund response to an angry customer, but a human support agent reads it and clicks "approve" before it sends. The AI drafts; the human makes the judgment call.
LLM (Large Language Model)
The engine underneath most AI tools you use. It's trained on enormous quantities of text to predict what comes next, an approach that turns out to be remarkably good at understanding and producing language.
Example: ChatGPT, Claude, and Gemini are all products sitting on top of LLMs. The LLM is the brain; the chat interface or app is the body.
MCP (Model Context Protocol)
A universal plug standard letting AI connect to different tools and data sources. USB-C, but for AI. Rather than every app building its own custom connector from scratch, MCP makes those connections reusable.
Example: Thanks to MCP, Claude can talk to your Notion, Gmail, and Google Calendar at once, instead of needing a separate bespoke integration for each.
Memory
An AI's ability to hold onto things between conversations, not just inside one. It can be built in or layered on top, and it's what makes an assistant feel persistent rather than like a stranger you meet again every time.
Example: You mention that you prefer short bullet-point answers. A week later, unprompted, it's still answering in bullets.
OpenAI Codex
OpenAI's model built specifically for reading and writing code. It powers GitHub Copilot and turns natural-language descriptions into working code across many languages.
Example: A PM types "create a function that takes a list of user emails and removes duplicates" and Codex writes it in Python, JavaScript, or whatever the project uses.
Prompt
The instruction or question you send an AI. Everything you type into the chat box is a prompt. How specific and well-structured it is directly shapes what you get back.
Example: "Write something about coffee" is vague. "Write a 200-word Instagram caption about oat milk lattes for a cosy café. Tone: warm and slightly witty." is a good prompt.
Prompt engineering
The craft of writing instructions that get the best possible output. It comes down to giving context, setting a role, specifying format, and offering examples. Copywriting, but for AI.
Example: Instead of "write a bio," a well-engineered prompt reads: "Write a 3-sentence professional bio for a UX designer with 5 years of experience. Tone: warm but confident. Audience: potential clients."
RAG (Retrieval-Augmented Generation)
A technique that hands the AI specific documents or data at the moment it answers. An open-book exam rather than one from memory. The result is more accurate and more current.
Example: A law firm's assistant uses RAG to search a decade of internal case files before answering, instead of guessing from general knowledge.
Sentiment analysis
AI reading the emotional tone behind text: positive, negative, neutral, or finer shades like frustration or delight. Useful for understanding how people feel at scale.
Example: A product team runs sentiment analysis across 10,000 app reviews and immediately catches a spike in negativity after a recent update, saving hours of manual reading.
Skills
Predefined capabilities you hand an agent: searching the web, writing code, reading files, sending emails. Modular "superpowers" you mix and match by task.
Example: A design AI gets three skills: access to a Figma library for existing components, brand guidelines to stay on-brand, and an image generation tool for mockups. Together they turn a one-line brief into a full draft layout.
System prompt
Hidden instructions written by the product team before any user shows up. They set the AI's role, personality, rules, and limits. Users rarely see them, but they shape everything the AI says.
Example: A banking chatbot's system prompt might read: "You are a helpful financial assistant. Never give specific investment advice. Always suggest speaking to a human advisor for complex queries. Use formal language."
Token
The unit AI uses to measure text. Not quite a word, not quite a letter. Roughly a syllable or a short word. Models cap how many tokens they can handle at once, and you're usually billed by the token. Longer prompts mean more tokens, which means higher cost.
Example: "I love great design" is about 5 tokens. A 10-page document might be 5,000. A 100,000-token limit is roughly a 75,000-word novel.
Tool calling
When an AI stops mid-task, reaches for an external tool (a calculator, a database, a web search), takes the result, and carries on. It's how AI moves from knowing things to doing things.
Example: You ask a design assistant to "check if this button colour meets accessibility standards." It pauses, calls a contrast checker with your hex values, gets the answer, and tells you whether it passes WCAG, rather than guessing from training data.
Training data
The vast body of content an AI learned from: books, websites, conversations, code. What went in shapes what the model knows, what biases it carries, and where it's strong or weak.
Example: If a hiring AI trained mostly on CVs from one demographic, it may quietly favour candidates who resemble that group, even though nobody set out for that to happen.
Vibe coding
Building software by describing what you want in plain language and letting AI write the code. You iterate through conversation instead of typing a single line yourself. Fast for prototypes, risky the moment it becomes production code without proper review.
Example: A product manager with no coding background builds a working clickable prototype of a new feature in an afternoon, just by describing it to a tool like Cursor or Bolt.