Everything a Senior Engineer Needs to Know About What's Inside an LLM

There's a specific kind of guilt every senior engineer carries around AI: you ship features that call an LLM API, you tune prompts, maybe you fine-tune something on a Friday afternoon, and yet you've never actually sat down and understood what's happening inside the model itself. TLDR Dev is trying to fix that with a five-part series, and Part Two just landed, focused entirely on architecture.

The centerpiece is "Attention Is All You Need," the 2017 paper that introduced the transformer and quietly rewired the entire field. Before that paper, sequence models leaned on recurrent networks that processed text one token at a time, which made them slow and bad at holding onto context over long stretches. Attention changed the math: instead of marching through a sentence step by step, the model looks at every token simultaneously and weighs how much each one matters to every other one. That parallelism is the whole reason today's models can be trained on oceans of data using GPU clusters instead of waiting in a serial queue.

The series doesn't stop at transformers, though. It also gets into diffusion models, the architecture behind most modern image generators, which work by learning to reverse a noise process rather than predicting the next token in a sequence. Putting transformers and diffusion side by side is a smart move, because it shows readers that "AI architecture" isn't one idea, it's a family of different mathematical tricks solving different problems, and knowing which one powers what you're using changes how you reason about its limits.

This is part of a bigger five-part arc: hardware first (TSMC, Nvidia, ASML, the TPU-versus-GPU debate), architecture now, then training, and presumably inference and applications to close it out. It's aimed squarely at engineers who've been treating LLMs as a black box API and want the layer underneath, without going back for a machine learning PhD to get it.