Advanced RAG Techniques: Architecture [2026]

About this course

This course contains the use of artificial intelligence.

This Advanced RAG Techniques course takes you from a first retrieve-then-generate program to a measured production RAG pipeline on one Python workbench.

You chunk documents, embed them, take top-k, and stuff the prompt. The system answers. Then a ticket asks what a company earned last quarter, and the retrieved sentence is true and missing the company name. That is an accuracy ceiling, not a model failure.

You do not need a prior RAG course. We start with what RAG is in plain words, then you build a first working program, then you measure every upgrade on the same corpus and the same question file. Engineers already running naive RAG can skip ahead after the baseline.

Anthropic described that identity failure in Introducing Contextual Retrieval on 19 September 2024. On their mix, contextual embeddings plus BM25 plus a reranker cut top-20 retrieval failure 67 percent (5.7 percent to 1.9 percent). Those are their numbers, dated September 2024, not a promise about your documents. This course teaches you to run the same kind of keep-or-kill test on yours.

Build a Measured Advanced RAG Pipeline on One Workbench

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