In this tutorial, I’ll guide you through creating an Amazon Bedrock Knowledge Base from start to finish. Learn how to leverage your proprietary data to enhance AI-generated responses through the powerful Retrieval-Augmented Generation (RAG) system. We’ll explore how Amazon Bedrock simplifies building a RAG system, from embedding documents to storing them in a vector database for optimized retrieval.
Here’s what we’ll cover:
1. What is RAG, and how does Amazon Bedrock streamline this process?
2. Building your own knowledge base in Amazon Bedrock, including:
• Setting up data sources and vector stores
• Configuring embedding models
• Testing knowledge base retrieval and text generation with a Large Language Model (LLM)
We’ll walk through real-world examples, such as retrieving insights from documents, demonstrating how to integrate external knowledge into your AI solutions seamlessly.
By the end of this video, you’ll have a fully managed Amazon Bedrock Knowledge Base set up, and you’ll see firsthand how it enhances AI accuracy by incorporating your custom data.
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