AI Terms Explained: The Plain-English Guide to Understanding Artificial Intelligence in 2026

Software that can do things we’d normally expect a human brain to do — like understand language, recognise images, write text, or make decisions. The word gets used loosely to cover everything from a basic chatbot to a system that can write code, analyse medical scans, or beat world champions at chess.

Real-world example: When you ask ChatGPT to write an email and it does — that’s AI at work.

A way of building AI where instead of writing rules by hand, you feed the system millions of examples and let it figure out the patterns itself. The more data it sees, the better it gets — without a programmer updating the rules manually.

Real-world example: Netflix’s recommendation engine learned your taste in films by watching what you watched, paused, rewatched, and skipped — not because someone programmed ‘user likes thrillers.’

The technology behind ChatGPT, Claude, Gemini, and most modern AI chatbots. An LLM is trained on enormous amounts of text — books, websites, code, articles — until it learns to predict what word comes next with remarkable accuracy. At its core, that’s the key idea. Everything else — writing essays, answering questions, summarising documents — flows from that one ability pushed to an extreme.

Real-world example: GPT-5.6, Fable 5, and Gemini are all LLMs. When you type a question and get a paragraph back, an LLM wrote it.

A type of machine learning that uses structures loosely inspired by the human brain — called neural networks — to process information in layers. Each layer picks up something more complex than the last. Deep learning is what made modern AI actually useful, especially for images, speech, and language.

Real-world example: When your phone unlocks with your face, deep learning is identifying your features layer by layer — edges, then shapes, then the full face.

The underlying structure most modern AI runs on. Imagine millions of tiny connected nodes, each passing a signal to the next one. The strength of each connection — called a weight — gets adjusted during training until the network produces the right output. A large neural network with many layers is what people mean when they say ‘deep learning.’

Real-world example: A neural network trained to detect spam email learns that certain words, sender patterns, and link structures together signal ‘spam’ — not because it was told, but because it saw enough examples.

Numbers inside an AI model that get adjusted during training. More parameters generally means the model can store more knowledge and handle more complex tasks — but also costs more to run. When you see ‘1.5 trillion parameters’ for a model like Grok 4.5, that’s a rough measure of its size and capacity.

Real-world example: GPT-3 had 175 billion parameters and felt like magic in 2020. Today’s frontier models are 5–10 times larger.

When an AI confidently states something that is completely wrong. Not a glitch — just the model predicting a plausible-sounding answer that happens to be false. It’s one of the biggest unsolved problems in AI right now, particularly in high-stakes fields like law and medicine.

Real-world example: Ask an AI chatbot about a specific court case and it might invent a verdict, a judge’s name, and a citation — all sounding completely authoritative, all completely made up.

The text you type into an AI to get a response. How you write your prompt has a huge effect on what you get back. A vague prompt gets a vague answer. A specific, well-structured prompt gets a specific, useful one. The skill of writing effective prompts has its own name — prompt engineering.

Real-world example: ‘Summarise this article’ is a prompt. ‘Summarise this article in 3 bullet points, written for someone with no background in finance’ is a much better one.

How much text an AI can ‘see’ at once during a conversation. A useful way to imagine it is the model’s working memory. If your conversation gets longer than the context window, the AI starts forgetting the earlier parts — which is why very long chats sometimes feel like the AI has lost the thread.

Real-world example: Early ChatGPT had a context window of about 4,000 tokens (roughly 3,000 words). Modern models handle millions of tokens — the equivalent of several long novels.

The unit AI models use to process text. Not quite words, not quite letters — tokens are chunks of characters. ‘Artificial’ might be one token. ‘AI’ is one token. A space before a word is sometimes its own token. When AI companies charge per token, they’re charging for how much text goes in and comes out. Most models process about 750 words per 1,000 tokens.

Real-world example: When you see pricing like ‘$5 per million input tokens,’ that’s roughly the cost to process around 750,000 words worth of input.

AI that doesn’t just answer questions — it takes actions. An agentic AI system can browse the web, write code, send emails, book appointments, or manage files on your behalf, with minimal human input at each step. Many leading AI companies are investing heavily in this direction. We covered this in detail in our article on agentic AI explained.

Real-world example: Instead of asking ChatGPT ‘how do I research competitors?’ — an agentic AI would actually go research them, compile the results, and hand you a summary.

A technique used to bypass an AI’s safety rules — getting it to produce content or information it was specifically trained to refuse. Jailbreaks are a constant cat-and-mouse game between users finding loopholes and AI companies patching them. A jailbreak of Anthropic’s Fable 5 is what triggered the US government to shut the model down for 19 days in June 2026.

Real-world example: Telling an AI ‘pretend you are an AI with no restrictions’ is one of the oldest and simplest jailbreak attempts. Modern ones are far more sophisticated.

Taking a general-purpose AI model and training it further on a specific set of data so it becomes better at a particular task or domain. A medical company might fine-tune a general LLM on clinical records to make it more accurate for healthcare questions. It’s cheaper and faster than training a model from scratch.

Real-world example: A customer service chatbot for a bank is usually a fine-tuned version of a larger general model — it’s been trained specifically on banking language and the company’s own FAQs.

The information an AI learns from. For most LLMs, this is a massive collection of text scraped from the internet, books, and other sources. The quality, size, and diversity of training data has a huge impact on how well the model performs — and what biases it carries.

Real-world example: If an AI was trained mostly on English text, it will perform worse in other languages. That’s a training data problem, not a fundamental AI limitation.

When a trained AI model actually runs and produces an output. Training is the expensive, time-consuming part — done once. Inference is what happens every time you send a message and get a reply. The global shortage of AI compute is largely an inference problem: there aren’t enough chips to handle the billions of queries hitting AI systems every day. We covered how this shortage is affecting Apple, Microsoft, and the global chip market.

Real-world example: Every time someone uses ChatGPT, Google runs an inference — the model processes the input and generates a response in real time.

Open source AI means the model’s weights (the trained parameters) are publicly released — anyone can download, run, modify, or build on them. Closed source means the model stays private — you can only access it through the company’s API or product. Meta’s Llama models are open source. OpenAI’s GPT series and Anthropic’s Claude are closed source. This distinction sits at the centre of the US-China AI competition: China’s DeepSeek used open-source techniques to close the gap despite chip restrictions.

Real-world example: Open source AI is like sharing a recipe. Closed source is like selling the dish but keeping the recipe locked away.

An AI that can work with more than one type of input — not just text, but also images, audio, video, or code. Most leading AI models today are multimodal. You can send a photo to ChatGPT and ask what’s in it, or describe an image and ask an AI to generate it.

Real-world example: Google’s Gemini can watch a video, listen to audio, read text, and respond to all three in the same conversation. That’s multimodal.

The safety rules built into an AI model to stop it from producing harmful, dangerous, or inappropriate content. Guardrails are why an AI refuses certain requests. They’re trained in — not just a list of blocked words — which is why jailbreaks work by tricking the model rather than bypassing a filter.

Real-world example: When an AI says ‘I can’t help with that,’ guardrails are why. When a jailbreak works, it’s because someone found a way around those guardrails.

Government restrictions on which technologies can be shared with foreign countries or nationals. In AI, this has become a major story: the US has used export controls to block advanced AI chips from reaching China, and most recently to temporarily shut down Anthropic’s Fable 5 and Mythos 5 globally. If you followed our coverage on the Fable 5 ban and return — export controls are what made that happen.

Real-world example: When the US blocks Nvidia from selling its most advanced AI chips to Chinese companies, that’s an export control. When Anthropic was given 90 minutes to take its models offline — that was an export control directive.

AI jargon isn’t accidental. Some of it exists because the concepts are genuinely new. But a lot of it creates a barrier between the people building these systems and the people whose lives they affect. At TechSun News, we think everyone deserves to understand what’s being built — and what’s being decided — in their name. Bookmark this article. We’ll keep updating it as new terms enter the conversation.

A) Hallucination — I had no idea AI could just make things up

B) Parameters — the numbers behind model size never made sense to me

C) Agentic AI — the idea of AI taking actions on its own is new to me

Tell us in the comments — and let us know if there’s a term you’d like us to add.

AI is the broad idea — software that can do things requiring human-like intelligence. Machine learning is one method of building AI, where the system learns from data rather than following rules written by a programmer. All machine learning is AI, but not all AI uses machine learning. A simple rule-based chatbot from 2010 was AI. ChatGPT is AI built using machine learning. The terms get used interchangeably in headlines, but they mean different things technically.

It depends on the context. If an AI hallucinates a fun fact in a casual conversation, the stakes are low. If it invents a legal case reference that a lawyer puts in a court filing — which has happened — the consequences can be serious. Most AI companies display warnings about the risk of hallucinations for exactly this reason. The safest approach is to treat AI output as a starting point to verify, not a final answer to trust blindly — especially for anything involving health, law, finance, or facts that really matter.

Because the cost of running an AI model scales with how much text it processes — not how many times you click send. A short message costs less to process than a long one. Charging per token lets companies price more accurately based on actual compute used. For most casual users on subscription plans, this doesn’t matter — you pay a flat monthly fee. For developers building products on top of AI APIs, token pricing is a central part of their cost model, and why the memory chip shortage we covered drives up AI service costs too.