--- title: "AGI to hallucinations: a plain-language glossary of the AI terms you keep hearing" description: "Artificial intelligence has flooded everyday conversation with jargon — AGI, tokens, hallucinations, agents, inference — that can leave even technically minded people lost. Here is a plain-language guide to the terms you are most likely to encounter, so the next headline, product launch or meeting makes a little more sense." category: "Technology" category_url: https://newsparlor.com/category/technology author: "Jasmine Howard" published: 2026-07-04T00:39:00.000Z updated: 2026-07-04T00:39:00.000Z canonical: https://newsparlor.com/article/agi-to-hallucinations-a-plain-language-glossary-of-the-ai-terms-you-keep-hearing tags: ["artificial-intelligence", "explainer", "glossary", "machine-learning", "llm", "technology"] --- # AGI to hallucinations: a plain-language glossary of the AI terms you keep hearing Artificial intelligence has flooded everyday conversation with jargon — AGI, tokens, hallucinations, agents, inference — that can leave even technically minded people lost. Here is a plain-language guide to the terms you are most likely to encounter, so the next headline, product launch or meeting makes a little more sense. Artificial intelligence is now discussed everywhere — in boardrooms, headlines, product launches and dinner-table arguments — and with it has come a blizzard of specialized vocabulary. Even people who work in technology can find themselves nodding along to terms they only half understand. Drawing on a recent guide from [TechCrunch](https://techcrunch.com/2026/07/03/artificial-intelligence-definition-glossary-hallucinations-guide-to-common-ai-terms/), here is a plain-language explainer of the words that come up most often. ## The big-picture terms **Artificial general intelligence (AGI).** The field's most contested phrase. It generally refers to a hypothetical AI that can match or outperform humans across most economically valuable work, rather than being good at just one narrow task. There is no agreed definition or test for it, which is part of why claims about how close we are should be treated with caution. **Large language model (LLM).** The kind of system behind assistants such as ChatGPT and Claude. LLMs are large neural networks trained on vast amounts of text to predict and generate language, which is why they can write, summarize and answer questions in fluent prose. ## How the models work **Token.** The basic unit an AI model reads and writes. Text is broken into tokens — chunks that are often parts of words rather than whole words — and the model processes and generates language one token at a time. Token counts are also how usage is often measured and priced. **Inference.** The stage where a trained model is actually put to work — taking an input and producing an output, such as answering your question. It is distinct from training, the earlier and far more resource-intensive process of building the model in the first place. **Reasoning.** A label for techniques, often called "chain-of-thought," in which a model breaks a problem into intermediate steps rather than jumping straight to an answer. This can improve performance on harder tasks, though it does not mean the system "thinks" as a person does. ## Building and refining systems **Fine-tuning.** Taking an existing model and training it further on a narrower, specialized dataset so it performs better at a particular job — for example, adapting a general model to legal or medical language. **Distillation.** A technique for transferring the capabilities of a large, powerful model into a smaller, cheaper one, so the smaller model can run more efficiently while retaining much of the larger one's ability. **AI agent.** A system designed to carry out multi-step tasks more autonomously than a simple chatbot — for instance, planning and executing a sequence of actions to complete a goal, rather than just responding to a single prompt. Agents are an area of intense development, and their reliability varies. ## The one everyone should know **Hallucination.** Perhaps the most important term for everyday users. A hallucination is when an AI model states false information as if it were true — confidently inventing facts, citations or details. It is a fundamental limitation of current systems, and the reason that anything important produced by an AI should be checked against a reliable source. ## Why a glossary helps None of these terms is beyond an ordinary reader, but their sheer number, and the speed at which new ones appear, can make the field feel more impenetrable than it is. Understanding a handful of core concepts — what a model is doing when it "infers," why it sometimes "hallucinates," what people mean (and do not mean) by "AGI" — is enough to follow most of the conversation critically. That critical distance matters. Much of the language around AI is generated by companies with something to sell, and clear definitions are a defense against hype. Knowing that "AGI" has no settled meaning, or that a fluent answer can still be a fabrication, helps separate what these tools genuinely do from what they are merely claimed to do — which, in a fast-moving field, is a useful skill for everyone. ## Sources - [The only AI glossary you'll need this year](https://techcrunch.com/2026/07/03/artificial-intelligence-definition-glossary-hallucinations-guide-to-common-ai-terms/)