RAG in Practice
Ground an LLM in your own documents — the pipeline behind reliable, up-to-date answers.
intermediate · 45 minutes
**Retrieval-Augmented Generation (RAG)** connects a language model to an external knowledge source so it can answer questions about private, current, or domain-specific data with citations. This course walks the full pipeline — ingest, chunk, embed, index, retrieve, rerank, and generate — and focuses on the practical decisions and failure modes that separate a demo from a production system. You will learn to diagnose bad answers, evaluate retrieval quality, and choose RAG over fine-tuning or long context when it actually fits.
What you'll learn
- What RAG Is and the Problem It Solves
- The RAG Pipeline End to End
- Embeddings, Similarity, and Hybrid Search
- Chunking, Vector Stores, and Retrieval Tuning
- Grounded Prompts, Citations, and Evaluation
- Failure Modes and RAG vs Fine-Tuning vs Long Context