Stage 3 · Building with AI
RAG & Vector Databases
Give models fresh, private knowledge through retrieval instead of retraining.
4 lessons1 ready
- 01 RAG Explained: Retrieval-Augmented Generation Chunking, embeddings, vector search, re-ranking and grounded answers with citations. Intermediate 25 min ✓
- 02 Vector Databases and Similarity Search Distance metrics, HNSW and IVF indexes, filtering and choosing a store. Advanced 20 min ✎ In progress
- 03 Chunking, Hybrid Search and Re-ranking The engineering choices that decide most of RAG quality. Advanced 15 min ✎ In progress
- 04 Evaluating RAG and GraphRAG Measuring retrieval and answer quality; when knowledge graphs help. Advanced 15 min ✎ In progress
Matching units in the new course
Each unit has goals, algorithms, must-read papers, a project and a self-test.