Generative AI is transforming how small and medium-sized businesses (SMBs) operate, from intelligent customer support and document search to knowledge management and business automation. While building a proof of concept is relatively straightforward, deploying a secure, scalable, and production-ready AI application presents a different set of challenges.

In this session, we'll explore how to take a Retrieval-Augmented Generation (RAG) application from prototype to production using AWS. We'll walk through the key architectural components, discuss security and governance best practices, and examine how AWS services can be used to build reliable, cost-effective GenAI solutions that scale with business needs.

Whether you're a developer, solutions architect, startup founder, or cloud engineer, you'll gain practical insights into designing and deploying production-ready GenAI applications on AWS.

What you'll learn:
1. What Retrieval-Augmented Generation (RAG) is and why it's a preferred approach for enterprise and SMB AI applications
2. How to design a secure, scalable RAG architecture on AWS
3. Best practices for document ingestion, embeddings, vector search, and prompt orchestration
4. How to use services such as Amazon Bedrock, Amazon S3, AWS Lambda, Amazon API Gateway, Amazon Cognito, and Amazon OpenSearch Service to build end-to-end GenAI applications
5. Security considerations including IAM, encryption, access controls, guardrails, and protecting sensitive business data
6. Strategies for monitoring, optimizing costs, and operating GenAI workloads in production
7. Common pitfalls when moving from proof of concept to production and how to avoid them

By the end of this session, you'll understand the architectural patterns, security principles, and operational best practices needed to build production-ready GenAI applications on AWS that deliver business value while maintaining security, reliability, and scalability.