information-retrieval

Search quality arguments end when there is a number, and begin again over whether the number is honest. This lesson builds offline evaluation from its atoms: a judgment set, precision and recall at k, MRR for known-item queries, and NDCG computed by hand for graded relevance. Then the online half: clicks, position bias, and why the top result gets clicked even when it is wrong.

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.

Matching finds candidates; scoring orders them, and the ordering is the product. This lesson builds BM25, the default ranking function of Lucene, Elasticsearch and OpenSearch, from its three ingredients: rare terms count more, repeated terms saturate, and long documents get discounted. With the formula, the two tuning knobs, and the saturation curve computed by hand.

Search does not scan documents; it looks up precomputed answers. This lesson builds the inverted index from first principles, then covers the pipeline that feeds it: tokenization, normalisation, stemming and synonyms, and why an analysis mistake made at index time cannot be fixed at query time. Includes the classic failure where a product SKU becomes unfindable.

Vector search exists because exact nearest neighbour search is intractable at scale and the curse of dimensionality defeats the classical index structures. This lesson covers distance metrics and when each is right, why brute force costs what it does, why k-d trees fail above a few dozen dimensions, and the recall-latency trade that every approximate index makes.

Deep dive into two advanced RAG architectures — HippoRAG's hippocampal-inspired knowledge graph indexing and RAPTOR's recursive summarization tree — and why both dramatically outperform flat vector retrieval on multi-hop questions.

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.

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.