Visualizers
Learn by moving things
Interactive explainers for the ideas behind retrieval, embeddings, and agents. A formula tells you what is true; dragging a vector shows you why. Each one also appears inside the article it belongs to, and runs entirely in your browser, so drag, poke, and break it.
Sentence-window retrieval
From: How RAG Reads a Corpus →Index individual sentences so the match is sharp, but when one matches, return it plus a window of the sentences around it, so the model reads the context the bare sentence lacked.
Each sentence is indexed on its own, so the match is sharp: only the sentence about the 30-day return matches.
Parent-document (small-to-big)
From: How RAG Reads a Corpus →Index small child chunks so matching is precise, but when a child matches, hand the model its larger parent chunk or whole section. You search small and read big.
Small child chunks are indexed and matched precisely: the “30-day defective return” child is the hit.
Summary indexing
From: How RAG Reads a Corpus →Index a short summary of each document so matching is fast and topical, but when a summary matches, retrieve the full source document behind it and read from that.
Only a short summary of each document is indexed, so matching is fast and topical: the IT policy summary wins.