Advanced RAG

Retrieval-Augmented Generation (RAG) is how you make large language models useful at enterprise scale. This course teaches you to build systems where an LLM can access fresh data—your data—without retraining or hallucinating.

You'll master embeddings, the bridge between raw text and semantic search. You'll learn document processing at scale (chunking, metadata extraction), then retrieval strategies that go beyond keyword search: hybrid methods combining semantic and keyword approaches, multimodal retrieval for images and text, ranking that surfaces the most relevant context.

Hands-on labs walk you through real architectures. By course end, you'll know how to wire a data source (database, knowledge base, documents) to an LLM so it grounds its answers in what's true. Weekly Q&A with industry experts keeps you current with evolving practices. Essential for AI engineers building production LLM applications.