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.
Late chunking
From: How RAG Reads a Corpus →Naive chunking cuts first and embeds each scrap blind to the rest. Late chunking flips the order: embed the whole document first, so every chunk's vector is informed by the full page, then cut and pool. The chunk keeps the context naive chunking throws away.
Naive chunking cuts first and embeds each scrap on its own, so the chunk's vector never learns the context that surrounded 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.