AI Glossary for non-techie people
AI can feel very intimidating - especially if you are just starting to explore it. We’re hoping that this glossary translates AI jargon into everyday language so you can confidently start navigating the landscape.
Some Core Concepts
Artificial Intelligence (AI)
Technology that lets computers do things we usually associate with human intelligence, like recognizing patterns, understanding language, or making decisions from data. Think of it as teaching computers to think and learn like humans do.
[Note that AI is different than “automation”, although some automated tasks and workflows may be supported through the use of AI]
Deep Learning
A type of machine learning that uses many‑layered “neural networks” (much like the human brain) to spot very complex patterns, especially in images, audio, and language.
generative ai
AI that can create new content - such as text, images, video, or music - by learning patterns from existing examples and then producing fresh variations.
Large Language Model (LLM)
A powerful AI system trained on huge amounts of text so it can understand prompts and generate human-like language, from answers and summaries to stories and code. *For example - Chat GPT is a commonly used example of an LLM.
Machine Learning (ML)
A way of building systems that learn from examples instead of being given step‑by‑step instruction - the computer finds patterns in data and uses them to make predictions or decisions. Results typically improve with more data processed.
*For Example: Your email spam filter learns what you consider spam by watching which emails you delete, getting better over time.
Neural Network / Artificial Neural Network (ANN)
A software system loosely inspired by the brain, made of many simple connected units (“neurons”) that work together to recognize patterns in data.
Working With AI
AI Agent
An AI system that can take actions toward a goal - like searching, calling tools, or triggering workflows, often with limited human supervision.
AI Automation
Using AI to handle tasks that used to require human effort - such as routing emails, drafting replies, or tagging content - often to save time and reduce errors.
chatbot
A chatbot is a computer program designed to simulate conversation with human users, usually through text or voice, acting as a virtual assistant that operates 24/7. It works by using artificial intelligence (AI) and natural language processing (NLP) to understand what you are asking and provide an appropriate answer.
GPT (Generative Pre‑trained Transformer)
GPTs can be your AI assistants - using an LLM like Chat GPT or Perplexity AI, you can train large datasets and then adapt it to generate fluent, human-like responses. Custom GPTs can generate superior outputs vs. a basic prompt - but they require configuration for specific prompts. GPTs require instructions, knowledge, and capabilities.
Think of a Custom GPT as a trained, specialized assistant, and a prompt as the instruction given to it.
Prompt
The message or instructions you give an AI - such as a question, request, or example - that tells it what you want it to do.
Prompt Engineering
The skill of writing effective instructions (prompts) to get the best results from AI. Better prompts = better outputs. This requires no technical expertise - just learning what works.
*Example: Instead of "write about marketing," try "write a 200-word Instagram post about sustainable fashion for millennials in a friendly, conversational tone."
Other Notable Terms
Algorithm
A set of rules or steps a computer follows to solve a problem or make a decision from data.
BIAs
Systematic unfairness in AI outputs, often caused by imbalances or stereotypes in the data the system was trained on.
dataset
A collection of examples - such as text, images, or records - that an AI system uses to learn or to be tested on.
deepfake
Fake images, videos, or audio created using AI that convincingly mimic real people. Major security threat.
For example: A video of your CEO appearing to authorize a wire transfer, but they never actually said it.
guard rails
Rules, filters, and safety checks built into AI systems to reduce harmful, unsafe, or off‑topic outputs.
Hallucination
When an AI confidently generates information that sounds plausible but is factually wrong, made‑up, or not supported by its sources.
Responsible AI / Ethical AI
Practices and guidelines that aim to ensure AI is safe, fair, privacy‑aware, and aligned with human values and laws.
Last updated February, 2026
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