A fast bilingual reference for how large language model work is structured in practice.
This page collects the core layers people usually need when working with LLM systems: model behavior, prompt structure, context setup, tool use, and repeatable agent workflow patterns. It is meant to be skimmed first and revisited as a working reference.
Good LLM outcomes rarely come from one clever sentence. They come from an aligned system that gives the model the right task, enough grounding, the right tools, and a clear operating frame.
The wiki frames practical execution too: let the model reason, let tools reach the environment, and let review loops catch weak assumptions before they become production mistakes.
Start here if you want the big picture first. The goal is to give teams a compact shared language for thinking about LLM systems, from instruction design to validation and execution.
These layers work together. The model brings capability, the prompt defines the task, and context supplies the background the model cannot safely infer by itself.
In real workflows, the model should not stay isolated in chat. Tools let it search, read files, edit code, run tests, and verify assumptions against the environment.
A harness turns raw model output into a usable operating system with permissions, checkpoints, review criteria, and repeatable handoff patterns.
This wiki reads best from model limits to prompt structure to tool-enabled workflow. That sequence makes it easier to turn abstract ideas into reliable project behavior.
The original PDF is embedded below. Use this page for a quick conceptual pass, then continue reading here or open the PDF separately for the full document.