TL;DR — A glossary to actually understand the terms you hit when reading about LLMs: token, embedding, attention, KV cache, GQA, MoE, quantization and the rest. But not alphabetical — in dependency order: every entry uses only concepts already explained above, so if you read it start to finish, by the time you reach "Mixture of Experts" you already have all the pieces to get it. The analogies come from the world of the people who hang around this blog: networks, caches, PID controllers, tuning. No math prerequisite beyond "I know what an array is."
Alphabetical LLM glossaries have a structural flaw: the "attention" entry sends you to "softmax", which is further down, which in turn uses "logits", which is earlier but assumes "vocabulary". You end up hopping around like a Wikipedia page at two in the morning.
This document is built the other way around: like a chain. It's the glossary I wish I'd had, and the analogies are the ones that made the concepts click for me — someone coming from sysadmin, networks and controllers, not a PhD in ML. If you too think in terms of caches, error signals and control loops, you're in the right place.
A note of honesty, before we start. This glossary doesn't come from knowledge I already possess. It comes from the questions I asked an LLM — "explain the KV cache to me", "why that √d?" — and from the explanations it wrote for me. Some sank in, others I'm still chewing on: I don't fully master everything you read here, and I won't pretend to. What I needed was a single place to come back to — a notebook to reopen when I run into one of these terms while reading something on the topic, to reread it slowly and with the analogies that pin it down for me. I've left it here in case it's useful to you too; but it is, first and foremost, mine.
