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Master Production-Ready RAG: Architect Reliable GenAI Systems with YugabyteDB and AWS

Modern AI apps are only as strong as their data architecture. As developers race to integrate LLMs like GPT and Claude into their applications, many run into a familiar wall: traditional database architectures weren’t built for AI-native workloads. From retrieval-augmented generation (RAG) to vector search, delivering fast, reliable, and scalable experiences requires rethinking how data is stored, accessed, and served.

This webinar explores the database architectural challenges of building apps with LLMs—and how to overcome them. Learn proven patterns for scaling, securing, and optimizing LLM-powered systems in production.

Register now to explore:

  • Database architectural patterns for AI-native apps
  • Designing with LLMs: RAG, vector indexing, memory stores
  • Ensuring resilience and scalability in geo-distributed systems
  • AI observability and feedback loops for continuous improvement

 

Fill out the form to get your complimentary copy of the webinar replay and discover how you can spearhead your business transformation.

 

Watch On-Demand

Speakers

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Karthik Ranganathan
Co-CEO & Co-Founder, Yugabyte

Karthik Ranganathan is Co-CEO and Co-Founder at Yugabyte, the company behind YugabyteDB, the open-source, high-performance distributed SQL database for building global, cloud-native applications.. Karthik was one of the original database engineers at Meta(Facebook), responsible for building distributed databases such as Cassandra and HBase. He is an Apache HBase committer, and also an early contributor to Cassandra, before it was open-sourced by Meta.

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Grant Liu
ISV Solutions Architect, Amazon Web Services (AWS)

Grant Liu is a Solutions Architecture leader at AWS who helps software companies build better businesses and products on AWS. Previous to AWS, he ran technical field teams at Hortonworks, CoreOS, and UnravelData helping companies accelerate their business outcomes via cloud infrastructure and data platforms. Feel free to tap him on the shoulder any time to chat about all things related to early and late stage startups.

FAQ

How do I build a reliable Retrieval-Augmented generation pipeline on AWS?

When combining AWS services with YugabyteDB, you can design a RAG pipeline that delivers low-latency retrieval, high availability, and geo-distributed resilience. The webinar walks through reference architectures and demos you can apply directly to your workloads.

What role does YugabyteDB play in scaling GenAI and vector search applications?

YugabyteDB provides a distributed, PostgreSQL-compatible database that supports both structured data and vector search at scale. Its built-in resilience and linear scalability make it ideal for powering AI applications with demanding performance needs.

Can I use PostgreSQL-compatible databases to power GenAI workloads?

Yes, you can! YugabyteDB is a PostgreSQL-compatible distributed SQL database purpose-built for GenAI workloads. It delivers the resilience, scalability, and low-latency vector search needed to power RAG pipelines, LLM-powered systems, and AI-native applications.

What are the best practices for making GenAI systems resilient and production-ready?

Best practices for building resilient GenAI systems include architecting for fault tolerance, using distributed databases, monitoring system health, and ensuring seamless scalability. This webinar shares proven patterns for deploying enterprise-ready GenAI systems.