FIRESIDE CHAT: RAG VERSUS CAG FRAMEWORK - CHOOSING THE RIGHT ARCHITECTURE FOR ENTERPRISE AI

As enterprises operationalize AI, the choice of architecture for knowledge-intensive applications has become a defining factor in performance, scalability, and trust. While Retrieval-Augmented Generation (RAG) has become a dominant approach by grounding LLM outputs in external knowledge sources, new models like the Context-Augmented Generation (CAG) framework are emerging - promising greater context-awareness, domain adaptability, and system efficiency.

Topics of discussion will include, yet will not be limited to:

• How RAG and CAG differ in design, workflow, and enterprise applicability
• When enterprises should favor retrieval vs. contextual augmentation
• Infrastructure, governance, and performance considerations for each framework
• Future directions in hybrid and next-generation architectures that combine the best of both worlds

Shailesh Kadam, Artificial Intelligence & Cloud Architect, SAKS GLOBAL

Sampath Madanu, Statistical Programming Associate Director - Programming Portfolio Lead, ASTRAZENECA

HYBRID Moderator/Speaker: Junaith Haja, Senior Data Engineer, AMAZON