A chat model only knows what it saw in training. RAG is the plumbing that lets it answer from your private documents, today's news, or anything it was never trained on: a second kind of model that turns text into coordinates, a database of those coordinates, and a search step that runs before the LLM ever sees your prompt.
A stack of transformer layers is not yet ChatGPT or Claude. This is the rest of the path: how text becomes tokens, how a raw next-word predictor turns into an assistant across three training phases, how LoRA customizes a model on a budget, and why everyone is racing for data and compute.
We use models built on transformers every day, yet the architecture itself usually stays a black box. It doesn't have to, and following it doesn't take heavy math. A visual, ground-up walkthrough: how words become numbers, how attention lets them shape each other, and how it all turns into a next-word prediction.
Reading notes from Addy Osmani on the difference between using AI as a tool and letting it quietly replace your thinking. Key research findings and heuristics worth keeping.
I've been shipping with AI tools for a year. At some point the gap between using them and actually understanding them stopped feeling fine. This is about what I decided to do about it.
The AI job market is noisy and competitive. Here are the ideas worth keeping from a talk by Andrew Ng and Laurence Moroney on careers, technical debt, and what it takes to ship real AI work.