vector-search
4 free lessons tagged vector-search across Computer Science, AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Lexical Meets Vector: Hybrid Search and Rank Fusion
Vector search did not replace keyword search, because the two fail in opposite places: BM25 cannot see that laptop and notebook mean the same thing, and embeddings cannot see that SKU-4471-B is not approximately anything. This lesson maps the two failure surfaces, then builds the production answer: run both retrievers and fuse the rankings, with reciprocal rank fusion done by hand.
GraphRAG: Knowledge-Graph Augmented Retrieval
Go beyond dense-vector search: learn how Microsoft GraphRAG extracts entities, builds a knowledge graph, clusters it with the Leiden algorithm, and serves both local and global queries with community summaries — delivering answers that classic RAG cannot.
RAG Query Rewriting: HyDE, Multi-Query, Decomposition, and Step-Back
Master four advanced query rewriting techniques that dramatically improve RAG retrieval quality: Hypothetical Document Embeddings, multi-query expansion, query decomposition, and step-back prompting. Learn when to reach for each and how to implement them.
RAG Chunking Strategies: From Fixed-Size to Late Chunking
A deep dive into how you split documents for retrieval-augmented generation — fixed-size, recursive, semantic, hierarchical, and late chunking — with concrete trade-offs and code for each approach.