Introduction
Artificial Intelligence (AI) is no longer on the horizon. It’s embedded in the way we work today. From healthcare improving patient diagnostics, to education using personalised learning platforms, to property and hospitality with dynamic pricing and booking chatbots, AI is now part of everyday business.
But with AI’s rapid growth comes a wave of new terminology. Acronyms and buzzwords like LLM, MCP, or Agent are everywhere. In news headlines, boardroom conversations, and client reports. If you’re not in the tech world every day, this jargon can feel confusing and even intimidating. I am in the tech space and I still wonder, where can I find a quick guide with what all of these acronyms mean?
That’s why we’ve created this AI Glossary: a simple, friendly guide to the most common AI terms. Whether you’re a solicitor, banker, hotel owner, estate agent, insurer, healthcare provider, or educator, this glossary will help you cut through the noise and understand what these terms really mean and why they matter.
Think of it as your quick reference guide whenever you see an unfamiliar acronym or phrase.
Glossary of Key AI Terms
AI (Artificial Intelligence)
Computer systems that can perform tasks usually linked with human intelligence, such as problem-solving, learning, or understanding language.
LLM (Large Language Model)
An AI model trained on massive amounts of text that can generate human-like responses. Popular tools like ChatGPT are based on LLMs and are already used for drafting documents, summarising information, and answering questions.
MCP (Model Context Protocol)
A standard that allows AI models to connect with tools, systems, or data securely. Think of it as a "universal adapter" for AI.
Agent
An AI “assistant” that doesn’t just answer questions but can take actions — such as retrieving information, sending emails, or booking appointments.
Machine Learning (ML)
A branch of AI where systems improve performance by learning from data. For example, fraud detection tools learn from past suspicious behaviour.
Neural Network
The structure that allows AI to mimic how the human brain processes information. It helps AI recognise patterns in speech, images, and text.
Generative AI
AI that can create content — from text and images to audio and video. This is the technology behind tools like ChatGPT, MidJourney, and DALL·E.
Prompt
The instruction or question you type into an AI tool to get a response. The clearer your prompt, the better the output.
Hallucination
When AI generates information that looks convincing but is incorrect or made up. Human oversight is essential.
Ethical AI
The practice of ensuring AI is used fairly, responsibly, and transparently — especially important in regulated industries like healthcare, law, and finance.
Training Data
The data used to “teach” an AI model. High-quality training data leads to more accurate results.
Token
A unit of text (like a word or part of a word) that AI models process. Token limits explain why some AI tools restrict response length.
Embedding
A way of converting text into numbers so AI can compare meanings. This is widely used in search, recommendation engines, and legal discovery.
RAG (Retrieval-Augmented Generation)
A method where AI pulls in real-time information from trusted sources before responding — reducing mistakes and improving accuracy.
API (Application Programming Interface)
A digital bridge that lets software systems — including AI — communicate. For example, connecting an AI chatbot to a booking or case management system.
Chatbot
An AI tool that communicates with people in natural language. Now common in customer service, hospitality, and financial apps.
Fine-Tuning
Customising an AI model with your organisation’s data — for example, legal templates, medical notes, or lesson plans.
Supervised vs. Unsupervised Learning
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Supervised: AI learns from labelled data (e.g., “this claim is fraudulent, this one isn’t”).
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Unsupervised: AI finds patterns in unlabelled data (e.g., grouping customers with similar behaviour).
Bias
When AI produces skewed or unfair outcomes due to flawed training data. Managing bias is critical in industries like lending, hiring, or healthcare.
Explainable AI (XAI)
AI systems designed to make their decision-making clear and understandable — crucial where accountability and trust matter.
Why This Matters Across Industries
AI isn’t just for tech companies — it’s becoming a competitive advantage across every sector:
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Law → faster case research, smarter contract analysis.
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Insurance → automated claims and better fraud detection.
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Banking → stronger compliance and risk assessments.
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Property & Real Estate → AI valuations, forecasting, and client chatbots.
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Hospitality → dynamic pricing, personalised guest experiences.
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Healthcare → supporting diagnostics, monitoring patients, reducing admin.
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Education → personalised learning, automated marking, content creation.
Conclusion
Understanding the language of AI is the first step to making sense of the opportunities it brings. This glossary is designed to help anyone and everyone across professional services, property, hospitality, healthcare, education, and beyond feel confident, informed, and ready for the AI-powered future. Don't get intimidated by the jargon, save this little glossary and get ahead of the curve!
Ifigenia Arampelou
