Wikipedia link race: choosing among thousands of options
The model chooses the next step from hundreds or thousands of links, demonstrating fast decisions with constrained outputs. The post includes a 4:04 video.
RAG vs. Jev+RAG Explained
The post clearly explains RAG vs. Jev+RAG, walking through the standard RAG pipeline: documents are chunked, embedded, stored in a vector DB, and top-k closest chunks are retrieved for a query.
RAG vs. Jev + RAG, clearly explained! In a standard RAG setup, documents are split into chunks, converted into embeddings, and stored in a vector DB. When a query arrives, the system embeds it and retrieves the top-k chunks with the closest vectors. Many production setups add
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