search
4 free lessons tagged search across Computer Science. 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.
Measuring Relevance: Judgments, NDCG, and the Click Trap
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.
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.
BM25: How Lexical Relevance Is Actually Computed
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.
The Inverted Index, and Why Analysis Decides Everything
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.