Model
A model is not a database of facts and not a search engine — it's a large file of numbers that encodes patterns absorbed from enormous amounts of text. Think of it as a very compressed snapshot of language: all the relationships, styles, and associations the model encountered during training, frozen into one file. When you use an AI, you're running that frozen snapshot.
Videos explaining this concept
E003Notes on AI
How AI Thinks
LLMs are prediction engines, not reasoning systems — they predict the next token based on everything they've been trained on. The difference from human thinking is not the mechanism but the grounding: AI is grounded in the textual representation of the world, humans are grounded in lived reality. AI knows "about" everything but experiences nothing — and that distinction matters more than most people realise.
E005Notes on AI
What Is a Model, Really?
A model is a file — a large set of numbers called weights that crystallize everything learned during training. The model doesn't store sentences or facts; it stores statistical patterns about how tokens follow each other. Training collapses into this artifact once, and once the file exists, copying it is cheap — which is why model security matters and why you can run an open-source model with no internet connection.
E006Notes on AI
Training vs Using a Model
Training and Inference are the two distinct phases of an AI model's lifecycle.
E010Notes on AI
The 5-Sentence Mental Model of GenAI
This episode provides a checkpoint after the foundational episodes, compressing the key concepts into five memorable sentences that serve as a mental compass for AI.
E011Notes on AI
Tokens
AI models don't read words — they read tokens, the basic unit of text a model processes. A token is close to a word but not identical: one word can be one token, several tokens, or several words can merge into one. Everything in AI is measured in tokens: input, output, context window size, and pricing. One token is roughly four characters in English; once you understand tokens, the limits and costs of AI stop feeling arbitrary.
E012Notes on AI
Tokenization
Tokenization is the process of turning raw text into tokens before an AI model processes it. It is preprocessing, not thinking — the model only sees the resulting pieces.