Retrieval-Augmented Generation — The part most people do badly
Everyone can build a RAG demo in an afternoon. Almost nobody can tell you why theirs returns the wrong chunk. The difference between those two people is this track — and the eval set at the end of it.
Track 2 of the AI engineering roadmap by K Laxman — about 10 hours across 1 stage.
RAG properly — all fifteen techniques
You can name, and choose between, the retrieval strategies. Not just 'embed, store, search'.
indexing · retrieval · generation · multi-query · RAG-Fusion · query decomposition · step-back prompting · HyDE · routing · query construction · multi-representation indexing · RAPTOR · ColBERT · CRAG · adaptive RAG
- Learn RAG From Scratch — LangChain / freeCodeCamp (video, free, ~2.2h, rated 10/10). The reason this beats every 'build a RAG bot in 10 minutes' video is the list of concepts on the left. It goes well past embed-and-retrieve into routing, decomposition, HyDE, RAPTOR and CRAG — the techniques you reach for when the naive version is returning garbage.
- langchain-ai/rag-from-scratch — LangChain (repo, free, ~4h, rated 9/10). Run every notebook. Watching RAG is not learning RAG — the chunking decisions only become real when your own document set makes them go wrong.
Build this — RAG over your own notes — plus a 30-question eval set. The bot is the easy half. The assignment is the eval set: thirty real questions with known answers, and a score you can quote. Without it you have a demo, and you will not be able to tell whether tomorrow's change helped.
You're done when: Given a wrong answer, you can separate retrieval failure from generation failure in under a minute.
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