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.
Adaptive RAG: retrieve only when it helps
From: RAG That Thinks →A router in front of retrieval decides whether a query even needs external knowledge. Simple questions go straight to the model; questions about private or fresh data get retrieved. It sizes the effort to the question.
Skip retrieval. the model already knows this; retrieval would only add noise and latency.
FLARE: retrieve mid-generation
From: RAG That Thinks →Forward-looking active retrieval woven into generation. As the model writes, confident words stream straight out, but whenever it is about to say something it is unsure of, it pauses, retrieves on that span, and continues.
Confident words are written straight out; retrieval only fires on the shaky ones.
Self-RAG: grade yourself as you write
From: RAG That Thinks →The model emits special reflection tokens that gate its own flow: whether to retrieve, whether a passage is relevant, and whether the answer is actually supported. A 'not supported' verdict sends it back to revise, in-band, before anything is shown.
Special reflection tokens decide whether to retrieve, whether a passage is relevant, and whether the answer is actually supported. A “no” on the last one sends it back to revise, in-band, before anything is shown.