RAG (Retrieval-Augmented Generation)-Generative AI

#ai #rochester #ieee #generativeai #rag #LLM #AWS #cloud #cloud-architecture

RAG (Retrieval-Augmented Generation)-Generative AI

Mahesh Soni

Abstract:  Generative AI models are powerful, but they often face challenges such as hallucinations and outdated knowledge. Retrieval-Augmented Generation (RAG) addresses these limitations by combining large language models (LLMs) with external knowledge sources, enabling accurate, context-aware responses. In this session, we will explore the fundamentals of RAG, its architecture, and why it is critical for enterprise AI solutions. We will discuss how RAG enhances LLM performance by retrieving relevant information from structured and unstructured data before generating responses. The session will cover key components such as document ingestion, embedding generation, vector databases, and integration with LLMs. We will also highlight AWS services like Amazon Bedrock, Amazon Kendra, and Amazon OpenSearch that simplify building RAG pipelines. By the end of this session, you will understand how to design and implement RAG-based applications to deliver reliable, domain-specific AI solutions.

Bio:  Mahesh Soni  is a Senior Solution Architect with over 20 years of experience in the technology field, specializing in cloud architecture, DevOps, AI/ML, data warehousing, security, and enterprise systems. He has led large-scale digital transformation, cloud modernization, automation, and intelligent system design initiatives across multiple industries, delivering secure, scalable, and cost-optimized solutions.  He holds a Bachelor of Engineering and a Postgraduate degree in Computer Science, with strong expertise in cloud migration strategies, AI-driven automation, FinOps-based cost optimization, and the design of resilient, event-driven, and microservices-based platforms. He brings deep experience in enterprise security, identity and access management, zero-trust models, and high-availability architectures.

Tuesday, March 10, 2026 (all times in EST):

7:00 PM - Welcome and Introduction

7:05 PM - Webinar by Mahesh Soni

7:50 PM - Q&A

8:00 PM - End of Webinar