World's Fair 2026
When All Context Matters: Extended Cache Augmented Generation - Luis Romero-Sevilla, Orbis
Overview
This talk introduces an extended Cache Augmented Generation (CAG) approach to address knowledge representation challenges when dealing with large, interconnected, and frequently updated document collections. The core idea is to leverage large context windows more effectively by distributing documents across multiple parallel CAG instances, allowing a supervisor model to query these instances and synthesize comprehensive answers. This method aims to overcome the limitations of simple RAG and the computational expense of GraphRAG in dynamic data environments.
Who should watch
- AI Engineers working with large datasets.
- Product Managers building AI-powered features that require up-to-date information.
- Builders facing challenges with data obsolescence and complex inter-document relationships.
- Those exploring alternatives to traditional RAG and GraphRAG for dynamic knowledge bases.
Key takeaways
- Simple RAG with vector databases struggles when all documents in a collection are relevant and the data updates frequently.
- GraphRAG is effective for interconnected data but becomes computationally expensive and slow when data is frequently recomputed.
- Cache Augmented Generation (CAG) uses models with large context windows, but filling the context can degrade answer quality.
- The proposed solution involves using multiple CAGs in parallel, distributing documents across different context "buckets."
- A supervisor model interrogates these buckets to build an understanding and synthesize answers, offering faster knowledge building than GraphRAG.
- Documents are distributed without a specific order, relying on the supervisor model to explore and find relevant information.
- KV cache costs can be managed by optimizing cache lifespan, and various retrieval strategies offer different trade-offs.
- There is no single, universally optimal solution; approaches must be tailored to specific problems.
Notable quotes
*The document in the collection becomes obsolete very fast and all documents get replaced with new information.*
*Recomputing a knowledge graph every time the data gets replaced is computationally very expensive, and it takes relatively long time.*
*Currently, there is no one-solution-fits-all. So, each type fits our solution to our very specific problem.*
Unofficial community note. Prefer the recording for nuance.