6 common AI terms explained: What are LLMs, AI Agents, Tokens…

Feeling overwhelmed by common AI jargon? Here are 6 basic terms in the world of generative AI that you can easily understand.

Artificial intelligence, particularly generative AI, has gone from being a mere buzzword to something that has become an integral part of our lives. Appliances now come with AI features. Google Search is getting AI Mode. Gemini is everywhere on Android. Apple Intelligence is here.

Here are some popular terms used in the world of AI. (Photo credit: Unsplash)

Shaurya Sharma

Major companies like Microsoft, Meta, and Google casually throw around AI jargon like it’s nothing. It can certainly be overwhelming for someone to come across terms like large language models (LLMs), tokens, AGI, AI agents, diffusion, hallucination, and other AI-related phrases.

Here, we simplify some of the most popular words frequently mentioned in the tech community.

Large Language Model (LLM)

You’ve probably heard of ChatGPT. The large language model working behind the scenes to power ChatGPT is part of the GPT family of models, such as GPT-3.5, GPT-4, GPT-4.5, and others. Other popular examples of LLMs include Meta’s LLaMA series of models, as well as Google’s Gemini 2.5 Pro and Gemini 2.5 Flash.

In essence, these large language models are systems trained on a vast amount of data. They develop an understanding of language and more. Simply put, it’s a piece of software trained on data in a way that enables it to understand what a human says to it. LLMs are mostly trained on publicly available data on the internet, text, language, and similar inputs.

Hallucinations

The term hallucination has a negative connotation, and for good reason. It refers to instances where these AI models, LLMs, malfunction or misbehave, generating inaccurate or completely false information.

The problem is that these models present inaccurate information to you as accurate. So they pretend something is real when it is not, and that is why the term 'hallucination' comes into play. It denotes believing something fake is real when it is not. For experts and industry insiders, this is a concern because it can have detrimental effects on humanity, and that is why there have been talks about placing more and more guardrails in place to keep these AI models in check.

AI Agents

AI agents, while not new, have suddenly become popular. Companies like Google and Microsoft are now actively talking about them. We saw some of the same at Google I/O 2025, as well as Microsoft Build 2025.

AI agents, simply put, are software with AI at their core, and they can make decisions on your behalf, work on your behalf, and take actions on their own to complete specific functions. In essence, these are pieces of software powered by large language models such as Gemini. They can accomplish a range of tasks, including writing code and building things. Companies can even incorporate AI agents to automate many processes, reduce monotonous jobs, and more.

Text-To-Image Models

AI models are of various types, and these text-to-image models are essentially software trained to convert text-based prompts into an image. Examples of these include Google's Imagen 3 and Imagen 4, as well as OpenAI's DALL-E. So, you must have seen this popular implementation in tools like ChatGPT, wherein people submitted text-based prompts to generate images, for instance, Ghibli-style images, or to generate an image from scratch by submitting a text-based prompt.

Text-To-Video Models

Just like text-to-image models, these models can essentially convert text-based prompts into videos. Examples of these include models from Google, such as its Veo 3, and OpenAI's Sora. These models seem to be advancing at a very fast rate. For instance, Google's Veo 3 can generate audio as well as video.

Tokens

While we have discussed large language models and how they work, and their nature, there is something called tokens, which acts as the backbone. A token, for those uninitiated, is a unit of data that an AI model processes. Generally, it is considered that the more tokens a model is trained on, the better it is and the more capabilities it has.

Tokens can be considered as words or the smallest unit that a model understands. For instance, a complex essay about the Mona Lisa can be broken down into individual tokens. So, each word could be a token. For example, the sentence 'Mona Lisa is a beautiful painting' can be broken down into individual tokens, such as 'Mona Lisa'. Then 'is' could be another token, 'a' another, and 'beautiful' another.

There is also something called an LLM context window, which is essentially the number of tokens that a particular large language model can understand in one prompt.

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