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9 areas · 140 lessons · free · no sign-up

AI engineering, explained from zero.

Start with what AI actually is. Finish able to build, test, secure and ship LLM apps, RAG systems and agents, and to answer interview questions on all of it. Every idea starts with an everyday picture, then real code.

Start with lesson 1 See the roadmap
Lessons
140
Topics covered
233
Playgrounds
19
Glossary terms
526

Roadmap

Nine areas, in the order to learn them

Foundations assumes nothing. Each later area builds on the ones before it; the lessons say what to read first.

  1. Area 00 · 11 lessons

    Foundations

    What AI is and how machines learn from examples, explained from zero. The maths and Python you need, and your first call to an LLM.

    0 / 11 done

  2. Area 01 · 20 lessons

    LLMs

    How large language models read, think and write: tokens, attention, sampling, prompting, fine-tuning and choosing a model.

    0 / 20 done

  3. Area 02 · 15 lessons

    RAG

    Retrieval-augmented generation: give a model the right documents at the right moment, from parsing and chunking to hybrid search, reranking and measuring retrieval.

    0 / 15 done

  4. Area 03 · 18 lessons

    AI Agents

    Models that use tools in a loop to get things done: the agent loop, memory, MCP, multi-agent patterns, frameworks, and keeping agents safe and affordable.

    0 / 18 done

  5. Area 04 · 13 lessons

    Evals

    How to know if your AI app is actually good: test sets, judges, RAG and agent metrics, evals in CI, tracing and the numbers to watch in production.

    0 / 13 done

  6. Area 05 · 15 lessons

    AI System Design

    Running LLM apps at scale: GPUs, serving, batching, caching, routing, rate limits, cost, and full interview-style designs.

    0 / 15 done

  7. Area 06 · 14 lessons

    AI Security

    Defending AI apps: prompt injection, guardrails, private data, safe tools and agents, secrets, supply chain, red teaming and responsible AI.

    0 / 14 done

  8. Area 07 · 18 lessons

    Deployment

    Getting AI apps from your laptop to users and keeping them healthy: Git, Docker, CI/CD with eval gates, Kubernetes, GPUs, safe releases and rollbacks.

    0 / 18 done

  9. Area 08 · 16 lessons

    Backend

    The backend skills every AI app sits on: HTTP, FastAPI, validation, async, streaming, auth, databases, caching, queues, reliability and testing.

    0 / 16 done

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Inside every lesson

The same shape every time, so you always know where you are

  1. 01

    Summary and analogy

    One plain sentence, then a picture from everyday life.

  2. 02

    The problem

    What goes wrong without this idea.

  3. 03

    How it works

    Small numbered steps, one idea each, with a diagram.

  4. 04

    See it in code

    A short runnable snippet with every line explained.

  5. 05

    On the real platform

    The real screen with numbered callouts, where a tool has one.

  6. 06

    A real example

    The idea inside a real product.

  7. 07

    The easy way

    The shortest route to start today, then the grown-up setup.

  8. 08

    Trade-offs and mistakes

    What you gain, what it costs, what beginners get wrong.

  9. 09

    Interview questions

    Model answers to compare yours against.

  10. 10

    Quiz and next steps

    Check it stuck, then see what to read next.