Best resources to learnRetrieval-Augmented Generation (RAG)

Study sheet

RAG grounds language-model answers in your own documents. You will learn embeddings, vector search, chunking, reranking and evaluation, and how to build and debug a production retrieval pipeline.

3 resources · Reviewed Oct 2026 · How we curate

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🎓Courses(2)

course

Building and Evaluating RAG Apps

Short DeepLearning.AI course with Jerry Liu and Anupam Datta covering two retrieval upgrades over naive RAG, sentence-window and auto-merging retrieval, plus the RAG triad of context relevance, groundedness, and answer relevance, measured in TruLens.

intermediate🇬🇧

course

Vector Databases from Embeddings to Apps

Explains how embeddings are formed, how similarity search algorithms index and query them at scale, and how vector stores underpin RAG. Hands-on labs build sparse, dense, hybrid, and multilingual search over real datasets using Weaviate.

intermediate🇬🇧

📄Papers(1)

paper

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

The 2020 paper introducing retrieval-augmented generation: a parametric seq2seq model combined with a non-parametric Wikipedia index accessed by a dense retriever, trained end-to-end. Explains why grounding generation in retrieved passages improves factual accuracy on knowledge-intensive tasks.

intermediate🇬🇧

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What are the best free resources to learn Retrieval-Augmented Generation (RAG)?

Dantes has curated 3 resources for Retrieval-Augmented Generation (RAG), including 2 courses, 1 papers. All resources are hand-picked for quality — no algorithmic filler. Browse the full list above to find the format that works best for you.

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Retrieval-Augmented Generation (RAG) is approachable at the beginner level — there are resources here specifically for those starting from scratch. As you progress, intermediate and advanced material is also available to take your skills further.

What types of Retrieval-Augmented Generation (RAG) learning resources are available on Dantes?

For Retrieval-Augmented Generation (RAG), Dantes has curated 2 courses, 1 papers. Each resource type serves a different learning style: videos and YouTube for visual learners, books for depth, courses for structured progression, and websites for quick reference.

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