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You’re probably hearing a lot of AI jargon, and you probably sort of know what some of it means . . . but not really. Below is an “explain it to me like I’m 5” definition of the 20+ most common AI terms, drawn from my own understanding, a bunch of research, and feedback from my most AI-pilled friends.
If you already know all this, no sweat, this post isn’t for you. For everyone else, keep the following list handy next time you’re in a meeting and you’re struggling to keep up with all the AI words flying around the room. I’ll continue adding to this list as new buzzwords emerge.
P.S. If you prefer, you can listen to this post in convenient podcast form: Spotify / Apple / YouTube.
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Model
An AI model is a computer program that is built to work like a human brain. You give it some input (i.e. a prompt), it does some processing, and it generates a response.
Like a child, a model “learns” by being exposed to many examples of how people typically respond or behave in different situations. As it sees more and more examples, it begins to recognize patterns, understand language, and generate coherent responses.
There are many different types of AI models. Some, which focus on language—like ChatGPT o3, Claude Sonnet 4, Gemini 2.5 Pro, Meta Llama 4, Grok 3, DeepSeek, and Mistral—are known as large language models (LLMs). Others are built for video, like Google Veo 3, OpenAI Sora, and Runway Gen-4. Some models specialize in generating voice, such as ElevenLabs, Cartesia, and Suno. There are also more traditional types of AI models, such as classification models (used in tasks like fraud detection), ranking models (used in search engines, social media feeds, and ads), and regression models (used to make numerical predictions).
LLM (large language model)
LLMs are text-based models, designed to understand and generate human-readable text. That’s why the name includes the word “language.”
Recently, most LLMs have actually evolved into “multi-modal” models that can process and generate not just text but also images, audio, and other types of content within a single conversational interface. For example, all of the ChatGPT LLM models natively support text, images, and even voice. This started with GPT-4o, where “o” stands for “omni” (meaning it accepts any combination of text, audio, and image input).
Here’s a really good primer on how LLMs actually work, and also this popular deep dive by Andrej Karpathy:
Transformer
The transformer architecture, developed by Google researchers in 2017, is the algorithmic discovery that made modern AI (and LLMs in particular) possible.
Transformers introduced a mechanism called “attention,” where instead of only being able to read text word‑by‑word, sequentially, the model is able to look at all the words at once. This helps the models understand how words relate to each other, making them far better at capturing meaning, context, and nuance than earlier techniques.
Another big advantage of the transformer architecture is that it’s highly parallelizable—it can process many parts of a sequence at the same time. This makes it possible to train much bigger and smarter models simply by scaling up the data and compute power. This breakthrough is why we suddenly went from basic chatbots to sophisticated AI assistants. Almost every major AI model today, including ChatGPT and Claude, is built on top of the transformer architecture.
This is the best explanation of transformers I’ve seen. Here’s also a more technical and visual deep dive:
Training/Pre-training
Training is the process by which an AI model learns by analyzing massive amounts of data. This data might include large portions of the internet, every book ever published, audio recordings, movies, video games, etc. Training state-of-the-art models can take weeks or months, require processing terabytes of data, and cost hundreds of millions of dollars.
For LLMs, the core training method is called “next-word prediction.” The model is shown billions of text sequences with the last word hidden, and it learns to predict what word should come next.
As it trains, the model adjusts millions of internal settings called “weights.” These are similar to how neurons in the human brain strengthen or weaken their connections based on experience. When the model makes a correct prediction, those weights are reinforced. When it makes an incorrect one, they’re adjusted. Over time, this process helps the model improve its understanding of facts, grammar, reasoning, and how language works in different contexts. Here&