Core AI Terms Glossary

New to AI? Start here. This section explains the essential AI terms you’ll come across — in plain language, with real-world examples. No technical background needed.

Artificial Intelligence (AI)

AI is technology that lets computers do things that normally take human thinking — like answering questions, writing, spotting patterns, or making decisions. It’s not magic. It’s software trained on a lot of data to get better at specific tasks.

💡 When you ask ChatGPT or Claude a question and it answers back, that’s AI in action.

Machine Learning

Machine learning is how AI gets smarter over time. Instead of being told exactly what to do, it learns by looking at thousands of examples — the same way a child learns what a “dog” looks like by seeing lots of dogs.

💡 Netflix uses machine learning to figure out what shows you might like, based on what you’ve watched before.

Deep Learning

Deep learning is a more advanced version of machine learning. It uses systems called neural networks (layers of code that loosely mimic how the brain works) to spot complex patterns — like recognising a face in a photo or understanding speech.

💡 When your phone unlocks by scanning your face, that’s deep learning doing the work.

Neural Network

A neural network is a computer system loosely inspired by the human brain. It’s made up of layers of connected “nodes” that pass information along, each one learning to spot something slightly different — until the full picture comes together.

💡 Think of it like a relay race. Each runner (node) picks up the baton and passes it on a little better than before.

Generative AI

Generative AI is AI that creates new things — text, images, music, video, or code — from scratch, based on a prompt you give it. It doesn’t copy and paste. It generates something new every time.

💡 ChatGPT writing you a marketing email, or Midjourney drawing a logo from your description — both are generative AI.

Large Language Model (LLM)

An LLM is an AI system trained on enormous amounts of text — books, websites, articles — so it can understand and generate human language. It’s the engine behind most AI chatbots and writing tools.

💡 ChatGPT, Claude, and Gemini are all examples of large language models.

Natural Language Processing (NLP)

NLP is the part of AI that helps computers understand human language — not just the words, but the meaning behind them. It’s why an AI can tell the difference between “I’m starving” and a request for food recipes.

💡 When you talk to a voice assistant and it actually understands what you meant, that’s NLP at work.

AI Model

An AI model is a trained system that takes in information and produces an output — an answer, a prediction, an image, a recommendation. Think of it as the brain behind an AI tool.

💡 When you use Claude or ChatGPT, you’re interacting with an AI model.

Training Data

Training data is the information an AI learned from before you ever spoke to it — millions of books, articles, websites, images, and more. The better the training data, the smarter and more accurate the AI tends to be.

💡 Just like a student learns from textbooks, an AI learns from its training data. Rubbish in, rubbish out.

Dataset

A dataset is a organised collection of information used to teach or test an AI. It could be thousands of photos, customer records, product reviews — any structured set of information an AI can learn from.

💡 Think of it as the AI’s study pack — everything it needs to prepare for the task.

Algorithm

An algorithm is just a set of step-by-step instructions that a computer follows to get a result. Every app, search engine, and AI tool runs on algorithms.

💡 A recipe is a great everyday example of an algorithm — follow these steps, in this order, to get this result.

Automation

Automation means getting technology to handle repetitive tasks for you, without needing to do them manually every time. It’s not AI on its own — but AI makes automation a lot smarter.

💡 An email that sends automatically when someone signs up to your newsletter is a basic form of automation.

AI Automation

AI automation takes regular automation a step further — instead of just following rigid rules, it can make decisions, adapt to different situations, and handle tasks that used to require a human brain.

💡 A chatbot that reads a customer complaint and drafts a personalised reply — without you lifting a finger — is AI automation.

AI Agent

An AI agent is an AI that can take action on your behalf — not just answer questions, but actually do things. It can browse the web, send emails, book appointments, or complete multi-step tasks with minimal input from you.

💡 It’s less like a calculator and more like a very capable assistant who can act independently once you point them in the right direction.

AI Assistant

An AI assistant is a tool that helps you get things done — answering questions, drafting content, summarising documents, or just saving you time. Claude, ChatGPT, and Gemini all fall into this category.

💡 Think of it as having a knowledgeable colleague available 24/7, who never gets tired and never judges your questions.

Prompt

A prompt is simply what you type to an AI — your question, request, or instruction. The quality of your prompt has a huge impact on the quality of what comes back.

💡 “Write me a blog post” is a prompt. So is “Write me a 500-word blog post for small business owners about saving time with AI — keep it friendly and jargon-free.”

Prompt Engineering

Prompt engineering is the skill of writing better prompts to get better results from AI. It’s not about coding — it’s about being clear, specific, and giving the AI the right context to do its job well.

💡 Anyone can learn this. The better your instructions, the better the output. It’s like learning how to give a good brief.

AI Hallucination

AI hallucination is when an AI makes something up — and sounds completely confident doing it. It might invent a statistic, a quote, or even a person that doesn’t exist. It’s not lying; it’s a genuine limitation.

💡 Always double-check any facts, figures, or names that an AI gives you — especially anything you plan to publish or share.

Context Window

The context window is the AI’s short-term memory. It can only hold so much information at once during a conversation. If a chat gets very long, earlier parts can “fall off” and the AI may forget what was said at the start.

💡 For long projects, it helps to paste in the key details again partway through a conversation — just to keep the AI on track.

Token

A token is a tiny chunk of text — roughly one word, or sometimes just part of a word — that the AI reads and processes. AI doesn’t read sentences the way humans do. It reads token by token.

💡 This matters mainly for understanding why very long conversations or documents can slow things down or hit limits.

Fine-Tuning

Fine-tuning is when an AI model is given extra training on a specific type of content — legal documents, medical notes, customer service conversations — to make it much better at that particular area.

💡 Think of it like a general-purpose chef who then does a specialised course in French cuisine. Same base skills, sharper focus.

Computer Vision

Computer vision is AI’s ability to “see” and make sense of images and video — identifying objects, reading text in photos, or spotting a face in a crowd.

💡 When Google Photos automatically groups photos by person, or a self-checkout scans your items — that’s computer vision.

Speech Recognition

Speech recognition is AI that converts spoken words into text or commands. It’s what powers voice assistants, meeting transcriptions, and live captioning.

💡 Dictating a message on your phone, or asking Siri a question — both use speech recognition.

AI Chatbot

An AI chatbot is a conversational AI you can talk to through text (or sometimes voice). It can answer questions, handle customer queries, and guide you through tasks — without a human on the other end.

💡 The chat bubble on a company’s website that answers your questions at midnight? That’s usually an AI chatbot.

Conversational AI

Conversational AI is the broader term for AI systems designed to hold natural back-and-forth conversations with humans. It goes beyond simple chatbots — it understands context, follow-up questions, and nuance.

💡 The difference between an old-school phone menu (“press 1 for billing”) and a modern AI that actually understands what you’re asking — that’s conversational AI.

Semantic Search

Semantic search understands what you mean, not just what you typed. Instead of hunting for exact keyword matches, it grasps the intent behind your question and finds more relevant results.

💡 Search “good shoes for sore feet” and get results for orthopedic footwear — even though you didn’t use that word. That’s semantic search.

Knowledge Graph

A knowledge graph is a map of connected information — people, places, topics, companies — that AI and search engines use to understand how things relate to each other, not just what they are individually.

💡 When Google shows a panel about a person with their job title, links to related people, and their notable work — that info comes from a knowledge graph.

AI Search

AI search gives you a direct answer instead of a list of links to scroll through. It reads multiple sources, pulls out the relevant information, and presents it to you in plain language.

💡 Ask Perplexity or Google’s AI Mode a question and get a summary answer with sources — that’s AI search.

AI Overview

An AI Overview is the summary box that appears at the top of some Google searches — a direct, AI-generated answer to your question, pulled from multiple websites.

💡 You’ve probably seen these already. They appear above the regular search results and give you the short answer before you’ve clicked anything.

Conversational Search

Conversational search lets you search the way you actually talk — asking follow-up questions, refining your request, and having a back-and-forth rather than starting a brand new search each time.

💡 “What’s a good CRM for small business?” → “Which of those works best with Gmail?” → “How much does that one cost?” — that’s conversational search.

AI Citation

An AI citation is when an AI tool references the source it pulled information from while answering a question. It’s AI’s way of showing its work.

💡 If an AI answer includes a link or says “according to [website]” — that’s a citation. It’s a good sign the answer is grounded in real sources.

Retrieval-Augmented Generation (RAG)

RAG is when an AI looks something up before it answers — like checking a book instead of guessing from memory. It connects the AI to external, up-to-date information so the answer is more accurate and relevant.

💡 If an AI can read your company documents and answer questions based on them, it’s probably using RAG.

Multimodal AI

Multimodal AI can handle more than just text — it can understand and create images, audio, video, and text all at once. “Multi” means many; “modal” means types of input.

💡 Upload a photo to Claude or ChatGPT and ask it to describe what’s happening — that’s multimodal AI in action.

AI Copilot

An AI copilot is an AI assistant built directly into the tools you already use — like Microsoft Word, Excel, or your email — to help you work faster without switching apps.

💡 Microsoft Copilot inside Word, helping you draft and edit without leaving the document — that’s an AI copilot.

AI Productivity Tools

AI productivity tools are apps and software that use AI to help you save time — drafting emails, summarising documents, automating tasks, organising notes, and more.

💡 ChatGPT, Claude, Notion AI, and Grammarly are all examples of AI productivity tools.

AI Workflow

An AI workflow is a sequence of tasks where AI tools work together to get something done — like drafting, reviewing, and scheduling content all in a connected chain.

💡 AI writes your social post → another tool resizes the image → a scheduler posts it automatically. That chain is an AI workflow.

AI Integration

AI integration is the process of connecting AI tools into the software or systems you already use — so they work together instead of separately.

💡 Connecting Claude to your Gmail so it can help you draft replies without switching tabs — that’s AI integration.

AI Analytics

AI analytics uses artificial intelligence to dig through data, spot patterns, and surface insights that would take humans much longer to find manually.

💡 An AI tool that scans your sales data and tells you which products sell best on which days — that’s AI analytics.

AI Image Generation

AI image generation creates brand new images from a text description — no design skills required. You describe what you want, and the AI draws it.

💡 “A friendly robot drinking coffee at a Cape Town café, in a watercolour style” — tools like Midjourney or DALL·E will turn that into an image in seconds.

AI Video Generation

AI video generation creates or edits videos automatically using text prompts, scripts, or existing clips — no video editing experience needed.

💡 Tools like Sora or Runway can turn a written script into a short video clip with visuals, voiceover, and music.

AI Copywriting

AI copywriting uses AI tools to help you write — blogs, emails, ads, product descriptions, social posts. It’s not about replacing your voice; it’s about getting a solid first draft fast.

💡 Give an AI your key points and target audience, and it’ll give you a draft to work from in seconds.

AI Literacy

AI literacy is understanding how AI works well enough to use it wisely — knowing what it can and can’t do, when to trust it, and when to question it. You don’t need to be technical to be AI literate.

💡 Reading this glossary is already a step toward AI literacy. You’re doing it right now.

Ethical AI

Ethical AI is about making sure AI is built and used in ways that are fair, honest, and safe — that it doesn’t discriminate, mislead people, or cause harm.

💡 Questions like “Could this AI tool make unfair decisions about job applications?” or “Is my data being used responsibly?” are ethical AI questions.

Bias in AI

AI bias happens when an AI produces unfair or skewed results because the data it learned from was unbalanced or flawed. If an AI mostly learned from one type of person, it may perform worse for everyone else.

💡 An AI hiring tool trained mostly on male CVs might unfairly rate female candidates lower. That’s AI bias — and it’s a real problem.

AI Visibility

AI visibility is how easily AI systems can find, understand, and reference your business or website when answering questions. The more visible you are to AI, the more likely it is to mention you.

💡 When someone asks ChatGPT to recommend an AI training provider and your business comes up — that’s AI visibility working for you.

AEO (Answer Engine Optimization)

AEO is about structuring your content so AI-powered search tools can easily find it, understand it, and use it to answer questions. It’s like SEO, but designed for the age of AI search.

💡 Writing clear, direct answers to common questions on your website gives AI tools something easy to quote or reference.

GEO (Generative Engine Optimization)

GEO focuses on making sure your content shows up inside AI-generated answers — not just traditional search results. It’s about being the source AI tools turn to.

💡 If an AI answer includes your brand’s name, statistic, or quote as a source — GEO is doing its job.

SEO (Search Engine Optimization)

SEO is the practice of improving your website so it appears higher in traditional search results on Google or Bing. It’s been around for years and is still important — but AI search is changing the game.

💡 Good SEO gets you found by people searching. Good GEO/AEO gets you found by AI answering.

Structured Data

Structured data is extra code added to a website that helps search engines and AI systems understand what the page is about — not just the words on it, but the meaning behind them.

💡 It’s like labelling your filing cabinet clearly, so anyone (or any AI) can find exactly what they’re looking for.

Schema Markup

Schema markup is a specific type of structured data that puts labels on your website content — telling AI and search engines: this is a business name, this is a price, this is a FAQ answer.

💡 It’s invisible to visitors but very helpful to AI crawlers reading your site.

Crawlability

Crawlability is how easy it is for search engines and AI bots to access and scan your website. If they can’t get in, your content won’t show up in search or AI answers.

💡 Think of it like making sure your front door is unlocked before inviting guests in.

Indexing

Indexing is when a search engine stores your website content in its database so it can show it in search results. If your page isn’t indexed, it essentially doesn’t exist to search engines.

💡 Getting indexed is step one. Ranking well in results is step two.

Entity SEO

Entity SEO is about helping AI and search engines understand your business as a real, recognisable thing in the world — not just a keyword. It means being clearly linked to a name, location, industry, and reputation.

💡 When Google’s knowledge panel shows your business photo, description, and reviews — entity SEO got you there.

E-E-A-T

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It’s the framework Google and AI systems use to decide whether your content is credible enough to show or recommend.

💡 Writing from real experience, backing up claims with sources, and having a clear author bio all help your E-E-A-T score.