AI and language models
Start with one question: how does a model turn some text into a prediction for the next token? From there, the rest of the terminology has something concrete to refer to.
Understand the model
Section titled “Understand the model”Transformers follows a prediction from token IDs to vectors, attention, and output probabilities. Small numerical examples explain the operations; optional sections give the formulas and code. See the reading order.
LLMs in the Wild covers the choices around that computation: architecture, prompts, training data, evaluation, and failure. Each article can also be read on its own.
Research papers, from first principles connects those operations to the original papers. Start with the DeepSeek-V4.1-Flash architecture walkthrough, or work through attention and recurrence with small numerical examples.
Pick a question
Section titled “Pick a question”| What do you want to understand? | Read |
|---|---|
| What do the matrix shapes and dot products mean? | Linear algebra |
| How does an error become a weight update? | Partial derivatives and backpropagation |
| Why use logarithms, softmax, and cross-entropy? | The math beneath |
| How can a model use words on both sides of a gap? | Masked language models |
| How do demonstrations and preferences change behavior? | Post-training |
| How does audio become a transcript? | Speech processing |
| How do models use tools or process images? | Agents and images |
Work through the code
Section titled “Work through the code”NumPy covers arrays and numerical operations. PyTorch introduces tensors and automatic differentiation. Use these when you want to reproduce a calculation; they are not prerequisites for the main reading.