Vector Databases from Embeddings to Apps
DeepLearning.AI
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.
More resources on Retrieval-Augmented Generation (RAG)
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.
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.