Concepts

AI, explained in plain language.

Short, honest explainers for the ideas behind modern AI. No jargon, no hype, no sign-up.

What is an LLM? Read →
The kind of AI behind chat assistants, explained without the jargon: what it is, how it produces text, and where it falls short.
How transformers work Read →
The neural network design that powers almost every modern language model, and the one idea, attention, that made it work.
Machine learning vs deep learning Read →
Two terms people use interchangeably that are not the same. How they nest, and what actually changed with "deep".
What are AI agents? Read →
The step from a model that answers questions to one that takes actions on your behalf, and why that step is harder than it sounds.
Open weights vs closed models Read →
Whether a model's trained parameters are public or locked behind an API changes who controls it, where it runs, and what you can do with it.
Inference and hosting Read →
Training builds the model once. Inference is every time you actually use it, and hosting is the infrastructure that keeps it fast.
How AI models are trained Read →
The process that turns a random network into something useful: read a lot, adjust, repeat, then teach it to behave.
What are tokens? Read →
Models do not see words. They see tokens, the fragments that pricing, context limits, and speed are all measured in.
What is a context window? Read →
The model's working memory: how much text it can hold in mind at once, and why it seems to forget once you go past it.
What is fine-tuning? Read →
Taking a capable general model and training it a little more on your own examples to make it better at one specific job.
What is RAG? Read →
Letting a model look things up. Instead of answering from frozen memory, it fetches the relevant text first, then answers from that.
What are parameters? Read →
The billions of numbers a model learns during training, and what a label like "7B" or "70B" is really telling you.