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.
The naive RAG pipeline
From: A Field Guide to RAG →Step through the four stages every RAG system runs: take the question, retrieve the relevant passages, put them in the prompt, and answer from them. A company-HR example makes each stage concrete.
Corrective RAG (CRAG): grade, then recover
From: RAG That Thinks →An evaluator grades the retrieved documents. When they look weak or only half cover the question, the system does not plough ahead; it refines the query and retrieves again before answering, instead of returning a confidently wrong answer.
Iterative / multi-hop RAG
From: RAG That Thinks →Some questions can only be searched for one fact at a time, because each answer is the key to the next question. Watch a three-hop chain: the company that built the iPhone, its founder, then the founder's successor.
Hop 1: retrieve “What company built the iPhone?”, get “Apple”, then feed that into the next question.