When All Context Matters: Extended Cache Augmented Generation - Luis Romero-Sevilla, Orbis
Jun 28, 2026 · 5:52
Luis Romero-Sevilla, VP of AI at Orbis Operations, presents Extended Cache Augmented Generation (ECAG) as a solution for knowledge representation when all documents in a collection are relevant and rapidly replaced. He argues that Simple RAG fails because it cannot pass all relevant documents to the LLM, and GraphRAG is computationally expensive due to constant graph recomputation. ECAG distributes documents across parallel KV-cache contexts (CAGs) and uses a supervisor model to interrogate each bucket, progressively building understanding without full recomputation. This approach balances speed and accuracy, outperforming GraphRAG on dynamic datasets while avoiding the degradation of single-context CAG. Romero-Sevilla notes that KV-cache costs can be reduced by optimizing cache lifetime.
