Master Amazon Bedrock Knowledge Bases: End-to-End RAG Explained скачать в хорошем качестве

Master Amazon Bedrock Knowledge Bases: End-to-End RAG Explained

Are your Large Language Models hallucinating or giving outdated answers? In this video, we dive deep into Amazon Bedrock Knowledge Bases—the fully managed, serverless way to build an end-to-end Retrieval-Augmented Generation (RAG) pipeline on AWS! 🚀 Learn how to securely connect your foundation models to your company’s private data sources (like Amazon S3, Confluence, SharePoint, or live databases like Redshift) so your AI can generate highly accurate, contextual, and verifiable responses—complete with source citations! In this video, we cover: Phase 1: Secure Data Ingestion - Connecting unstructured and structured data sources (S3, Redshift, etc.). Phase 2: Parsing & Chunking - How Bedrock extracts text from complex documents and intelligently splits it using semantic chunking. Phase 3: Embedding & Storage - Converting text to vectors and storing them in managed databases like Amazon OpenSearch Serverless or Pinecone. Phase 4: Runtime Retrieval & Generation - How the RetrieveAndGenerate API augments prompts to deliver accurate, cited answers. Whether you are building a customer support bot, an internal Q&A tool, or an AI agent, Amazon Bedrock automates the heavy lifting of RAG infrastructure. 🔔 Don't forget to LIKE and SUBSCRIBE for more exciting content on Enterprise AI and AWS! #AWS #AmazonBedrock #GenerativeAI #RAG #MachineLearning #ArtificialIntelligence #TechTutorial