https://video.ut0pia.org/videos/watch/8b899353-7b26-4c0e-b7f2-79b8a027e3e6 This session addresses a critical challenge in knowledge representation: extracting accurate answers from a rapidly changing dataset where every document is highly interconnected and relevant. Explore the limitations of standard retrieval methods for dynamic, high-context scenarios—including the constraints of Simple RAG and the computational bottlenecks of constantly recomputing a GraphRAG. To overcome these hurdles, this talk introduces a novel solution: Extended Cache Augmented Generation (ECAG). Speakers: Luis Romero-Sevilla (Orbis Operations): Luis Romero-Sevilla is an AI strategist and full-stack software engineer with over 13 years of experience driving mission-critical technological innovation across defense, healthcare, and the public sector, currently serving as the Vice President of AI at Orbis Operations. X/Twitter: https://x.com/lurose15 LinkedIn: https://www.linkedin.com/in/luis-romero-sevilla/ GitHub: https://github.com/lurose5 Mon, 29 Jun 2026 15:56:52 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.ut0pia.org https://video.ut0pia.org/lazy-static/avatars/0287a09a-aae7-4840-9843-b416426e7046.webp https://video.ut0pia.org/videos/watch/8b899353-7b26-4c0e-b7f2-79b8a027e3e6 All rights reserved, unless otherwise specified in the terms specified at https://video.ut0pia.org/about and potential licenses granted by each content's rightholder.