Every field accumulates jargon, and AI/LLM engineering has accumulated it faster than most — partly because the field is young, and partly because terms get reused across research papers, vendor marketing, and production engineering with slightly different meanings each time.
This is the reference I wish existed when I started building AI infrastructure: one place with the terms that actually come up in day-to-day work, grouped by where you'll encounter them, explained at the level a backend engineer needs — not a research-paper level, and not a marketing-page level.
Core model concepts
Token — The basic unit a language model reads and generates. Not quite a word, not quite a character — usually a sub-word chunk (e.g. "tokenization" might split into "token" + "ization"). Every cost, context limit, and latency figure you'll deal with is measured in tokens, not words or characters.
Context window — The maximum number of tokens (input + output combined) a model can attend to in a single request. Everything the model "knows" about the current conversation has to fit inside this window — anything older gets truncated or has to be summarized.
