Building and Evaluating Advanced RAG Applications

Building and Evaluating Advanced RAG Applications is a free, ~2-hour DeepLearning.AI short course taught by Jerry Liu (co-founder/CEO of LlamaIndex) and Anupam Datta (TruEra co-founder, now AI Research Lead at Snowflake), and for engineers who have already built a basic RAG pipeline it is one of the most focused, high-signal introductions to RAG evaluation available. Across six short video lessons and four runnable notebooks it teaches two retrieval upgrades that reliably beat baseline RAG (sentence-window and auto-merging retrieval) and a concrete evaluation framework, the 'RAG triad' of Context Relevance, Groundedness, and Answer Relevance, measured with TruLens/TruEra. Its real value is the eval-and-iterate loop most RAG tutorials skip, not breadth of coverage. The biggest caveats are honesty issues with positioning and aging code: the official page labels it 'Beginner', but Class Central's editors and the prerequisites (solid Python plus prior RAG experience) make clear it is not a true beginner course, and learners report TruLens/LlamaIndex version-compatibility breakage plus a non-pip-installable 'utils' helper that makes local replication outside the provided environment frustrating. Treat it as a sharp, dated-but-still-relevant supplement rather than a from-scratch RAG bootcamp.

It is an excellent, free, expert-taught primer on RAG evaluation and advanced retrieval, but only if you already understand and have built a basic RAG pipeline; it is short (no end-to-end project), the code now suffers from library version drift and a non-standard 'utils' package, and despite the official 'Beginner' tag it is genuinely intermediate. Right fit for that audience, wrong fit for true beginners.

Best for: Developers and ML/LLM engineers who already know Python and have built (or at least understand) a basic RAG pipeline and now want to measure and systematically improve it: people frustrated by inconsistent retrieval quality who need an evaluation methodology (the RAG triad) and two proven retrieval upgrades. It is also a fast, free way to evaluate LlamaIndex + TruLens before adopting them, and a good complement after a broader LLM course.

Skip if: Complete beginners to RAG, LLMs, or Python, despite the official 'Beginner' label, you will struggle without prior RAG context. Also not ideal for people who want a long, comprehensive, project-based build (this is ~2 hours with no capstone), those who need a verifiable, credit-bearing certificate, anyone wanting framework-agnostic theory (it is tightly coupled to LlamaIndex and TruLens), or learners who need fully reproducible, currently-maintained code, as some notebooks rely on a custom 'utils' helper and specific library versions that have drifted.