Udemy – Vector Databases & RAG: Build Semantic Search with LLMs

“This course contains the use of artificial intelligence.” Stop copying RAG code without understanding the retrieval system underneath it. Vector databases are a foundational technology for semantic search, retrieval-augmented generation, recommendations, similarity matching, and other modern AI applications. This course gives you a practical, vendor-neutral introduction to the concepts that make those systems work. You’ll progress from vectors and embeddings through similarity measurement, nearest-neighbor retrieval, metadata filtering, vector indexes, RAG architecture, database selection, and retrieval evaluation.

Who this course is for:

• Python and software developers entering AI application development
• Data and ML engineers new to vector retrieval
• Developers building semantic search or RAG applications
• Technical product builders evaluating vector-database technology
• Students who have followed AI tutorials but want to understand the underlying retrieval architecture