Engineers who have built or are about to build a retrieval system and want the whole picture quickly. It suits anyone interviewing for an AI or platform role, backend engineers asked to add search over documents, and people who have wired up a vector store but could not defend their chunk size or explain why recall is poor.

Each lesson opens with a short set of reading cards on the concepts it covers, then puts you straight into questions. Every question is multiple choice, and a wrong answer returns an explanation immediately — so the mistake becomes the thing you remember rather than something you mean to look up later.

Accuracy is tracked per lesson, so after one pass you have a map of which areas you are solid on and which need another round. That matters more here than in a long course: the point of a crash course is to find your gaps quickly, not to grind evenly through material you already know.

The first three lessons — Limitations of Pure LLMs, RAG vs Alternatives and What RAG Adds — are free, with their reading cards and their full question sets. They are a fair sample rather than a teaser: the format, the difficulty and the explanation style are identical to the 33 lessons behind them, so you can judge the whole course from them.

A video course is measured in hours watched; this is measured in questions answered. Watching someone explain chunk-size tradeoffs, hybrid retrieval, and evaluating retrieval separately from generation feels productive, but you find out whether you understood it the moment you have to choose between four plausible answers. Recall beats recognition, and questions force recall.

The trade-off is honest — a video series is better when a topic is entirely new to you. That is why this course assumes prior exposure to RAG systems, and why the full course exists for the areas where you want more room.

There is also a scheduling argument. 324 questions at roughly forty-five seconds each is about 4.0 hours, which fits into a few evenings around a job. A full syllabus at ten questions per sub-concept does not, and a candidate with an interview next week needs the version that finishes.

The difference is depth, not coverage. Both reach the same areas. This one gives you one merged lesson per cluster and 324 questions; GenAI Engineering: RAG Systems breaks the same material into 151 lessons at roughly ten questions each, so you can sit on a single idea until it is automatic. Start here if you are short on time or want to find your gaps, then move to the full course for the areas the gaps turn up in. If you want a different subject at the same pace, Python Crash Course follows the same format.

A conventional RAG syllabus teaches fixed-size chunking, recursive chunking and semantic chunking as three lessons, each restating why chunk boundaries matter at all. Merged into one, the actual decision — what you trade away at each boundary strategy — is visible in a single pass instead of three.

A practical order: work straight through without stopping to perfect anything, note the lessons where your accuracy drops below about seventy percent, then do a second pass on only those. Most people find three or four weak areas, not fourteen, and knowing which three is worth more than another ten hours of undirected study.

What this deliberately does not do is teach RAG systems from nothing. Every lesson assumes you can already read the syntax and follow the basic vocabulary, and spends its questions on the reasoning instead. If that assumption does not hold yet, the full course is the better starting point and this one will still be here afterwards.