Steven Chim chimurai

GH-600 Public Study Guide

Markdown-only workbook for Exam GH-600: Developing in Agentic AI Systems.

Last updated: May 24, 2026.

This version is organized around the official GH-600 domains. Each domain explains the concepts, shows the GitHub implementation artifacts, and includes examples you should be able to read in YAML, Markdown, CLI output, PR timelines, and audit logs.

Public sharing note: this guide is not an exam dump and does not contain real exam questions or answer choices. It is a structured study workbook built from official Microsoft and GitHub documentation, with practical examples written for learning and review.

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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