LLM + Control Tutorial

An applied tutorial covering LLM fundamentals, API usage, and agentic workflows, with a control-systems angle.

📖 Live site: https://markjh2001.github.io/LLM-Control-Tutorial/

What's covered

• Getting Started — conda environment, API keys.
• LLM Basics — what an LLM is, tokens, sampling, context windows.
• Calling the API — OpenAI, DeepSeek, and Qwen via one SDK; first call, unified client, streaming, tool use.
• Agentic Workflows — plan/act/observe loops, memory, multi-agent patterns.
• LLM + Control (in progress) — applying these patterns to control-systems problems.

Providers

The tutorial uses the openai Python SDK as the common wire format for OpenAI, DeepSeek, and Qwen / DashScope. Code that differs per provider is shown in three tabs, so you can pick whichever you have a key for.

Local development

conda activate llm-tutorial
pip install -e .
mkdocs serve

Open http://127.0.0.1:8000.

First-time setup (create the conda env):

conda create -n llm-tutorial python=3.12 -y
conda activate llm-tutorial
pip install -e .

Building

mkdocs build --strict

Static site is emitted to site/. Pushes to main trigger the GitHub Actions workflow which builds and publishes to GitHub Pages.

API keys

Copy .env.example to .env and fill in whichever provider key(s) you have. The .env file is git-ignored. See Getting Started → API Keys for details.

License

Content and code in this repository are released under the terms set by the repository owner; check with the owner before reusing.