What an LLM Actually Does

Pretraining, Prompting, Sampling, and Alignment By the end of this post, you'll understand what an LLM actually learns during pretraining (ontologies, math, pronoun resolution, all of it) and why this happens from nothing more than predicting the next word. You'll know the three architectural families of LLMs (decoder-only, encoder-only, encoder-decoder) and when each one fits the job. You'll see how unrelated tasks like sentiment analysis, question answering, and classification all get cast as conditional generation. You'll understand prompting, in-context learning, and why system prompts are longer than you'd expect. You'll know the difference between greedy decoding, random sampling, and temperature sampling, and why the obvious strategy is actually a bad one. Finally, you'll understand the three stages of training that take a raw pretrained model and turn it into something useful and safe: pretraining, instruction tuning, and preference alignment. One thread runs through all of thi