I wrote a 100-topic AI engineer roadmap that runs offline, with no API key

Every time I wanted to learn RAG, agents or MCP properly, I ended up with twelve browser tabs and no order to read them in. So I wrote the order down, and then wrote the lessons.

ai-engineer-roadmap is 100 topics in 7 phases, from "what is RAG?" to production AI. If you know basic Python, you can start today.

Repo: https://github.com/techsaurabh-394/ai-engineer-roadmap

The 7 phases are RAG Foundations (20 topics), Advanced RAG (20), LLM Concepts (14), Agentic AI (16), Frameworks (8), MCP (7) and Production AI (15). Topics are marked beginner, intermediate or advanced, so you can skip what you know.

git clone https://github.com/techsaurabh-394/ai-engineer-roadmap.git cd ai-engineer-roadmap python3 -m venv.venv && source.venv/bin/activate pip install -r requirements.txt pytest -q python 01-rag-foundations/code/mini_rag.py "Why use multi-stage builds?" Enter fullscreen mode Exit fullscreen mode The last command runs a complete, tiny RAG pipeline. It chunks three sample documents, scores them against your question and prints the best passage with its source file. The demo scripts in each code/ folder use only the Python standard library, so there is no API key and no network needed. If you set ANTHROPIC_API_KEY and install anthropic, Claude writes the answer from the retrieved passages instead.

CI checks that every local link in the Markdown pages points to a file that exists, validates the SVG diagrams, runs the tests and builds the TypeScript MCP example. I also went back and corrected my own claims when they did not match the repo: an earlier version said 47 interview questions, and the real count is 43 plus 4 system-design prompts.

If you find a mistake, a broken link, or a topic that is missing, open an issue or a pull request. The glossary and the study guide are good places to start. If it helps you, a star helps other people find it.