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Learning Roadmaps

AI Engineer

Go from Python basics to building real AI systems: understand how LLMs work, master prompting, then build RAG pipelines and AI agents step by step.

7 courses~8h total100% free, certificate included with every course

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Complete all courses in this roadmap to earn its badge.

Start with step 1

Courses in this roadmap

  1. Step 1Beginner

    Interactive Python Practice

    Learn Python programming with hands-on exercises. Practice variables, data types, functions, loops, and more with live code execution in your browser. No installation required - powered by Pyodide.

  2. Step 2Beginner40 min

    How LLMs Actually Work: Tokens, Parameters, and MoE Explained

    Go one level under the hood of ChatGPT, Claude, and Gemini. Understand tokens, next-token prediction, context windows, model parameters, Mixture of Experts, and why AI models cost money to run. A short, no-code course for people who use AI every day.

  3. Step 3Beginner~5h

    Prompt Engineering Course: Master AI Prompts with Hands-On Practice

    Master the art of crafting effective AI prompts through hands-on exercises. Learn prompt structure, few-shot learning, chain-of-thought reasoning, and advanced techniques with instant feedback. Build real-world prompts for code generation, content writing, and data analysis.

  4. Step 4Intermediate

    Vector Databases: The Foundation of AI Apps

    Master vector databases for AI applications. Learn embeddings, similarity search, and hands-on setup of Pinecone, pgvector, and Chroma. Understand indexing strategies, hybrid search, performance optimization, and how to choose the right database for your use case.

  5. Step 5Beginner75 min

    Local RAG for Beginners: Build a Private Knowledge Base

    Build a private, offline knowledge base over your own notes and PDFs using a local AI model. A hands-on beginner micro course: run a model with Ollama, turn documents into embeddings, store them in a local Chroma database, and ask questions answered from your own files. No cloud APIs, no fees, your data stays on your machine. Want to deploy it as a web app instead? See the Full-Stack RAG course (/courses/fullstack-rag-nextjs-supabase-gemini).

  6. Step 6Beginner35 min

    Build Your First AI Agent in 30 Minutes

    Build a working AI agent from scratch in Python. Learn the think-act-observe loop, call an LLM, give your agent a tool it can use, and handle a failed tool call. Framework-agnostic and beginner-friendly.

  7. Step 7Intermediate

    Agentic AI with Python — LangChain & LangGraph

    Build autonomous AI agents with Python using LangChain and LangGraph. Learn tool calling, stateful workflows, RAG-powered agents, multi-agent systems, and production deployment. From the ReACT pattern to a full customer support agent capstone.