How to Build RAG Applications with Snorkel AI and AWS

This 15-minute technical demo walks through building and deploying an end-to-end Retrieval-Augmented Generation (RAG) system using Snorkel AI, AWS, and Anthropic. Follow the complete RAG pipeline implementation from unstructured data ingestion through document chunking, embedding generation, vector store integration, and retrieval-based prompting for LLM inference. Designed for teams scaling GenAI capabilities in enterprise environments, discover how Snorkel Flow orchestrates a modular, composable RAG stack with AWS integration. Learn to leverage foundational models like Claude from Anthropic with real-time retrieval techniques for grounded, reliable generative responses. The demonstration covers RAG system architecture fundamentals, embedding generation with vector databases, Snorkel Flow integration for labeling and pipeline automation, and practical considerations for production deployment. Perfect for professionals focused on optimizing LLM performance, reducing hallucinations, or designing secure enterprise AI workflows. The content progresses through introduction, demo structure, agentic systems, use cases, automated data labeling, system comparison, synthetic data integration, and concluding insights.