Data Leaders Panel

The Future of Retrieval-Augmented AI: A Blueprint for Scalable and Cost-Effective RAG Systems

Amey Desai
Head of AI, Nexla
Naveen Pentakota
Associate Director of Data Science, Novartis
Han Wang
Lead Engineer, GenAI, Tecton

Learn from industry experts as they share actionable insights into optimizing RAG architectures for enhanced performance, integration with existing data infrastructures, and the strategic choices driving innovation in this space.

Retrieval-Augmented Generation (RAG) is revolutionizing the way organizations harness the power of AI for precision and efficiency. In this webinar, we explore the latest advancements in RAG systems, focusing on designing scalable, cost-effective solutions tailored to diverse enterprise needs.

Discover how cutting-edge retrieval mechanisms are transforming model training and deployment, delivering contextually rich outputs with reduced computational costs. Learn from industry experts as they share actionable insights into optimizing RAG architectures for enhanced performance, integration with existing data infrastructures, and the strategic choices driving innovation in this space.

Key Takeaways:

1️⃣ Scalability: Best practices for architecting scalable RAG systems that grow with your business.

2️⃣ Cost Efficiency: Techniques for reducing operational costs without compromising performance.

3️⃣ Industry Applications: How RAG is reshaping AI applications across industries with practical use cases.