What Does All These AI Acronyms Really Mean?

Helena McAleer

October 19, 2026
17 min read

I come from a marketing and innovation background, and in marketing there is ono end of acronyms, and it drove me crazy. Every conversation felt like we were speaking in a different language. So when I stepped into the world of AI, the thing that bugged me most wasn’t the technology, it was all these new acronyms I had to pretend I understood (again).

LLM, RAG, RLHF, MCP, AGI. Every conversation these days seems to arrive pre-loaded with letters, and everyone nods along as if they had always known what they meant. I nodded too, for a while, and then I got fed up, come on people speak English.

Here is what I’ve eventually worked out. If an acronym makes you feel behind, that says nothing about you and a great deal about the industry. Most of these terms describe ideas that are perfectly simple once someone says them in plain English. So that’s what this article is: a no BS AI jargon translation guide, written by someone who needed one.

Let’s Start With The Newest Debate: AI versus SI

Before we begin, you may have noticed a new pair of letters sitting alongside AI: SI, short for Super Intelligence. The idea is a system that would think better than any human across the board. According to IBM, it is still a hypothetical future state, meaning it does not exist.

As someone trained in marketing, I recognise a rebrand when I see one. What he (who shall not be mentioned) is trying to do with this renaming is create a distraction from reality and if you ask me, you can ignore it completely, unlike all the other terrible things he does.

The Basics

Let’s work methodically through these, if you are new to AI, these first few terms are what you may be hearing more and more often.

AI (artificial intelligence). This is the technology that lets computers do things we normally associate with human thinking, such as learning from experience, understanding language and solving problems.

Machine learning (ML). Instead of a person writing out every rule, the computer is shown lots of examples and works out the patterns for itself. Think of teaching a child to recognise a dog by showing them many dogs, rather than handing them a rulebook on ears and tails.

Neural network. It is loosely inspired by the way the brain is wired, with layers of connected points that pass information along and together spot patterns in complicated data.

Deep learning. A neural network with many layers stacked up, which is where the word “deep” comes from. According to IBM, some form of deep learning sits behind most of the AI we come across in daily life.

Model. The finished result of all that learning. When someone says “the model”, they mean the trained system you actually use, in the same way a recipe is the finished result of lots of testing in the kitchen.

Training data. The examples the model learned from. It might be text, images, audio or numbers, and the quality of what goes in has a big effect on the quality of what comes out.

The Ones You Hear In Meetings

This is where the letters really start flying. Here is what they are pointing at.

GenAI (generative AI). AI that generates something new, such as text, images, audio or video, in response to a request from you. IBM describes it as deep learning models that produce original content when given a prompt. If AI is the whole bookshelf, GenAI is the part that writes the books.

LLM (large language model). The engine behind most of the chat-style assistants people use. It is trained on enormous amounts of text and works by repeatedly predicting the next word in a sequence based on that data. The “large” refers to the sheer volume of text it learned from, and the “language” is exactly what it sounds like.

GPT (generative pre-trained transformer). A particular type of LLM, and the name that became famous through ChatGPT. Pre-trained means it has already done its learning before you meet it, and a transformer is the design that lets it keep track of how words relate to one another across a whole passage.

NLP (natural language processing). The older and broader field of getting computers to understand and use human language. The ability to recognise, understand and generate text and speech. It is the reason your phone can understand “set a timer for ten minutes”, and it paved the way for LLMs.

Chatbot. A program you talk to in everyday language. Older ones followed fixed scripts, while newer ones are usually powered by an LLM, which is why they feel so much more natural.

Copilot. A name for an AI assistant built into the software you already use, with the idea that you stay in charge and it sits beside you. Microsoft uses it as a product name, and the word is also used more loosely.

Multimodal. Able to work with more than one kind of information, such as text, images, audio and video, rather than just one. In practice it means you can show an AI a photo and ask a question about it.

Talking To AI

These are the words you will actually use, even if you never realised they had names.

Prompt. Whatever you type or say to an AI. A question, an instruction, a pasted-in email with a request attached, all of it counts. It is simply your side of the conversation.

Token. The small pieces an AI breaks your words into before it can work with them. These can be whole words, parts of words or single characters. Think of them as the building blocks of language the model reads and writes, which is why you will see AI usage measured in tokens rather than words.

Context window. How many tokens the AI can hold in view at one time, the maximum number of tokens a model can see and use when generating a response. Picture the size of a desk: a bigger desk means you can spread out more documents at once before something falls off the edge. It is also why a very long conversation can lead an AI to lose track of what you said earlier.

Prompt engineering. A grand name for something quite ordinary: getting better answers by being clearer about what you ask. You can shape your AI responses just by changing how you phrase the prompt, for example by telling it what role to take or what tone to use. If you have ever written a good brief for a colleague, you already have the skill.

Tip: you can go back and edit prompts as much as you like until get get the response you are after.

When Things Go Wrong, Or Get Better

These terms describe the common problems with AI and the common ways people try to fix them.

Hallucinations. When an AI produces something that is false or misleading but sounds perfectly confident. This is the model generating information that is wrong while still sounding plausible. It is the main reason to check anything important before you rely on it.

Why does it do this? It’s predicting, not looking things up. A large language model is essentially a very sophisticated prediction machine. It’s like the person in the bar who has the answer to everything, but is rarely right, there’s always one.

Bias. AI learns from human-made material, so it can pick up and repeat the unfairness found in that material. The more biassed the material, the more biased the model, the models can reflect and even amplify the biases in their training data.

How to prevent bias? Better data and careful testing help, but humans are innately biased therefore it is something to stay alert to always.

Fine-tuning. Extra training that turns a general-purpose model into a specialist. A model trained on broad general knowledge can be fine-tuned on legal questions and answers to become a chatbot for the legal field. It’s why you are seeing so many AI business appear now, they have fine-tuned an AI model to do a very specific task or understand a specific industry.

RLHF (reinforcement learning from human feedback). A way of improving a model by having people rate its answers, so it learns to prefer the ones humans judge better. It is a large part of why assistants feel helpful and polite rather than random.

RAG (retrieval augmented generation). Letting an AI look things up in a trusted source before it answers, rather than relying only on what it learned in training. This connects a model to outside knowledge so its answers can be more relevant and accurate, without retraining it.

Tip: Add sources to your prompts if you want your AI to give you more accurate feedback

Guardrails. The rules, standards and checks put around an AI system to keep it safe and ethical. Like the barriers on a motorway, they are there to keep things on course rather than to slow you down.

The Newer Buzzwords

AI is a relatively new industry therefore new terms are popping up all the time. These are the terms most likely to turn up in your inbox this month.

Agent and agentic AI. An AI agent is a program that can go off and complete a task for you without the need to be present, rather than just answering a question. It is the difference between an AI telling you the best time to climb Mount Everest and an agent booking your flight and your hotel. “Agentic AI” usually means several agents working together to complete a bigger task.

AGI (artificial general intelligence). A system that could learn and apply knowledge across a wide range of tasks as well as, or better than, a human. We are in theoretical territory now, and no known system comes close. When you hear someone say AGI is just around the corner, it is worth remembering that nobody has built it yet.

MCP (Model Context Protocol). A shared standard that lets AI connect to the places where your information lives, such as your files, calendar or business software. Think of it as a USB-C port but for your AI: one standard connector instead of a different cable for everything.

API (application programming interface). The agreed way for one piece of software to ask another for something. This is a contract that lets software access data or functions provided by other software. When a website offers “Pay with PayPal”, an API is doing the talking behind the scenes, and the same idea is how businesses plug AI into their own systems.

Shadow AI. When people use AI at work without their organisation knowing or approving it. This is the unsanctioned use of any AI application by employees without the formal approval or oversight of the IT department. It is rarely done with bad intent. People usually turn to these apps as they are familiar to them or to work faster, often because the approved options feel insufficient or too slow, but since nobody in IT knows about them, sensitive company information can end up somewhere it shouldn’t. It says less about people doing something wrong and more about a gap between what they need and what their workplace has provided.

This is the biggest risk to organisations as it can’t protect that information or show it’s following data protection rules like GDPR.

Inference. The moment an AI actually does its work and produces an answer, as opposed to the earlier phase where it learned. This happens one small piece at a time, with the model making its best guess at each step. Every time you press enter on a prompt, that is inference.

Vibe coding. Building software by describing what you want in plain language and letting AI write the code. It has opened up software creation to people who can’t code, while also raising questions about quality and security when nobody checks the result.

Is the skill of coding dead? Highly unlikely

Skills. Reusable instructions that teach an AI how you like a particular job done. Anthropic describes them as bundles of instructions and resources that Claude loads when they’re relevant to a specialised task. You set up your approach once, such as your house style or the format of a report, and it is applied whenever it fits, instead of you explaining it again each time. Think of it as the induction pack you’d hand a new colleague. ChatGPT uses the same word, and OpenAI describes its skills as reusable, shareable workflows that help it complete specific tasks more consistently.

Connectors (Claude). The way an AI links up to the apps and services where your information lives, such as Google Drive or Slack. Anthropic’s documentation says connectors extend Claude by connecting it to outside tools and data sources. Instead of copying and pasting between apps, you connect once, sign in and approve the access, and Claude can work with what’s inside. They run on MCP, the standard described earlier in this section.

Artifacts (Claude). Something Claude builds for you alongside the chat, such as a document, a small app, a presentation or a design, which you can open in its own space, edit, share and return to later. ChatGPT has its own vocabulary for similar ideas, and OpenAI has been changing it, so these names may shift.

Apps (ChatGPT). OpenAI describes apps as connecting ChatGPT to services you already use, such as Google Drive or Slack, so you can work with their information in a conversation. This is what used to be called connectors, and OpenAI’s release notes say connectors now appear in the directory as apps.

Plugins (ChatGPT). A bundle put together for a particular job. OpenAI explains that an app connects ChatGPT to a service such as Slack, while a plugin brings apps and skills together for a task. Some plugins contain only skills.

GPTs (custom GPTs). Not to be confused with the GPT models above. OpenAI describes a GPT as a version of ChatGPT configured for a specific purpose, combining particular instructions, knowledge and capabilities. OpenAI’s help page also says it plans to retire custom GPTs in favour of plugins, so this one may be on its way out.

See why it’s so hard to keep up…

Canvas (ChatGPT). OpenAI describes canvas as an interface for working with ChatGPT on writing and coding projects that need editing and revisions. It plays a similar role to Claude’s artifacts: a workspace beside the chat for something you’re building.

Rules & Responsibility

The final group is about how AI is meant to be used well, and who gets a say in it.

Responsible AI. An approach to building and using AI so that it is fair, secure, accountable and respectful of people’s privacy. Explainability, fairness, robustness, transparency and privacy among its common values.

Put simply, it is the question of whether you would be comfortable explaining to a customer how the AI reached its answer, would you?

Explainability (XAI, or explainable AI). Being able to understand and trust why an AI produced a particular result. These are a set of methods that let people interpret and understand what algorithms produce. The European Commission points out that it is often impossible to find out why an AI system made a decision, which becomes a real problem if the decision was about a job application or a loan.

Tip: We are in new territory here, and unfortunately we are seeing this play out live in the recruitment process. So my suggestion here is: always keep a human in the loop, we can’t 100% trust AI, and possibly never should.

Human in the loop (HITL). Keeping a person involved in what an AI does, so that someone checks, approves or can step in before anything important happens. If you are using AI, then you are the human in the loop, actively taking part in the running, supervision of and/or decision-making of the automated system, with the aim of getting the speed of automation without losing human judgement and ethics.

If you’ve ever had a colleague proofread a campaign before it went live, you already understand the idea. It also appears in law: The EU AI Act requires high-risk AI systems to be designed so that people can effectively oversee them.

EU AI Act. Europe’s rulebook for AI, and the first comprehensive legal framework on AI anywhere in the world, according to the European Commission. It works on a sliding scale: the riskier the use of AI, the stricter the rules, with certain practices banned outright and most everyday uses left largely alone. It became applicable on 2 August 2026, with some parts phased in later, so if you work with AI in Europe it is worth keeping an eye on.

SI (superintelligence). The idea of an AI that outthinks humans at absolutely everything. It doesn’t exist, nobody can say when or whether it will, and it has found its way into an impressive number of keynote slides. Think of it as the industry’s way of keeping us all gazing at the horizon rather than at what is on the desk in front of us. You can safely file it under “interesting, but not relevant to your Tuesday” and carry on.

Before you go

Behind every acronym in this article is an idea you could explain to a friend over coffee. The letters were never the clever part. They are just a shortcut that somehow turned into a barrier. So the next time someone fires a string of capitals at you in a meeting, you are allowed to say, “Sorry, what does that mean in plain English?” or “what does that mean in the context of this organisation (or conversation)”. The chances are good that half the room has been wondering the same thing. Understanding the words is the first step to feeling confident using AI, and confidence is what actually moves things forward.

Helena McAleer is co-founder of TheGenAIAcademy.com, helping organisations close the global skills gap in Generative AI through practical education, expert-led training, and responsible implementation strategies. Find out more at thegenaiacademy.com

Related Courses (more ways to learn)

Mastering Responsible AI
https://thegenaiacademy.com/courses/mastering-responsible-ai/

Critical Thinking For The AI Era
https://thegenaiacademy.com/courses/critical-thinking-for-the-ai-era/

AI Unlocked
https://thegenaiacademy.com/courses/ai-unlocked/

What Does All These AI Acronyms Really Mean?