854+ practice questions from actual Applied AI loops, with answers that get offers.
Every question ordered easy → hard into a journey per topic. Written by engineers who sat the loops at the labs hiring right now.
Not sure which of those you need? Follow the map instead: the four stages of preparation, and where to join them given what you already know.
The content is the product.
A real, unlocked card from the RAG & Agents journey:Customer's RAG pipeline started hallucinating after a data refresh — debug it live
Resist the urge to touch the prompt. A data refresh changes retrieval, not generation — so measure retrieval before you theorize.
# golden set recall, before touching anything hits = retriever.search(q, k=20) print(recall_at_k(hits, gold)) # 0.31 — was 0.86
Recall cratered. The refresh re-ran ingestion but skipped re-embedding, so the index points at stale vectors.
From “I can program” to “I build with models.”
Long-form and deliberately timeless: no model names to go stale, no invented numbers, and a real project thread running through each course, because reading about a loop and watching your own repeat a failing call are different kinds of knowing. 30 of the 76 lessons are free.
Applied AI Engineering
A structured path from writing normal software to building and shipping systems on top of language models. Assumes you can program and nothing about AI. Eight modules, from what the job actually is through to the interview that gets you it.
Build retrieval over your own documents, give it a tool, then harden it for a bad day.
Agent Engineering
Agents are the most over-applied pattern in applied AI and the hardest to make reliable. This course covers the loop, tool design, memory, multi-agent coordination, and evaluation, with as much attention on when not to build one as on how.
Build an agent loop from scratch, make it survive being killed mid-action, then measure it.
Not a wall of answers. A system that learns how you learn.
Reading answers from an LLM is passive, and it forgets you the moment you close the tab. This is built on knowledge tracing, the same learning science behind the best intelligent tutoring systems. It quietly models how well you recall each topic and points you to exactly what to study next.
- ▸Every concept closes with a self-check drill, so you practice active recall instead of just reading.
- ▸One tap after each answer (Got it / Shaky / Review) feeds a live mastery estimate per topic.
- ▸“Your next focus” sends you to your weakest area, not a random question.
- ▸Your mastery and journey persist, so every session builds on the last.