DL.AIHands-on reviewed
DeepLearning.AI
Building and Evaluating Advanced RAG Applications
For: Engineers whose basic RAG works in dev but fails in prod
Advanced · ~1.5 hours (5 lessons) · Free
Best RAG courses (2026)
RAG looks easy in a demo and fails in production. These are the courses that teach the failure modes.
RAG (Retrieval-Augmented Generation) is one of the most-deployed and most-misunderstood patterns in applied LLMs. Most teams ship a basic "embed → retrieve → stuff into context" pipeline, watch it work on demo questions, and then watch it hallucinate on real user queries.
The reason: the embed-retrieve-stuff naive pipeline misses 30-50% of relevant context in real corpora, and the LLM happily generates plausible-sounding answers from the wrong context. The fix is evaluation-driven retrieval engineering — and *that's* what the courses below teach.
We don't recommend any course that stops at "here's how to use a vector database." The vector-db part is the easy 10% of RAG. The interesting 90% is chunk strategy, embedding selection, query rewriting, reranking, and evaluation — all covered in the LlamaIndex + TruEra course we feature.
Pre-requisites
Recommended courses (1)
Filter all rag systems courses →Frequently asked questions
Do I need RAG, or can I just stuff everything into the context window?+
With 200K+ context windows on Claude and Gemini, "just stuff it" works for surprisingly large corpora. Use the rule: if your relevant corpus is under 100K tokens and stable, skip RAG. Above that, or if the corpus changes (e.g., per-user data, freshness-sensitive), you need retrieval. The cost calculation also matters — stuffing 200K tokens into every query is expensive.
Which vector database should I learn?+
For learning, pick one of Chroma (free, local) or Pinecone (free tier, hosted). For production, the choice matters less than people think — Pinecone, Weaviate, Qdrant, pgvector, and Vertex AI Vector Search all work fine at most scales. The differentiator is your reranking and evaluation, not the vector store.
How do I know if my RAG is actually retrieving the right context?+
This is the central question and it requires evaluation infrastructure. The DeepLearning.AI / TruEra course covers context-precision, context-recall, and answer-faithfulness metrics. Beyond the course, the practical answer: build a held-out evaluation set of 50-200 question-answer pairs, measure retrieval and answer quality on every change, and instrument with LangSmith or TruLens.
Related agents & tools
Once you've learned the concepts, these are the agents and tools where the skills pay back.
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