This session demonstrates how to build real-world Generative AI applications using Retrieval-Augmented Generation (RAG). While Large Language Models are powerful, they often lack domain-specific knowledge and can produce hallucinated responses. This session addresses these limitations by integrating LLMs with external data sources through vector search.

Attendees will learn how to design and implement a RAG pipeline, including document ingestion, embedding generation, semantic retrieval, and response generation. A live demonstration will showcase a working AI application that answers questions based on custom documents, highlighting the difference between traditional LLM responses and RAG-based grounded outputs.

The session is designed to be practical (with LIVE DEMO), platform-agnostic, and applicable across cloud ecosystems such as Azure, Google Cloud, and OpenAI.