Generative AI Applications with Amazon Bedrock

Step up your generative AI skills with this course focused on building generative AI applications using Amazon Bedrock. Building on the fundamentals, you'll dive deep into advanced features and services that enable more complex solutions. The course begins with a deep dive of Amazon Bedrock Knowledge Bases, teaching you how to enhance AI responses using Retrieval-Augmented Generation (RAG). You'll learn to create and manage knowledge bases, integrating them into your applications to provide context-aware, domain-specific AI interactions. Next, you'll learn Amazon Bedrock Prompt Management and Flows, enabling you to design intricate workflows that chain multiple AI operations. This section empowers you to create more sophisticated generative AI behaviors in your applications. The latter part of the course focuses on generative AI-driven agents for task automation. You'll learn to configure and deploy Bedrock Agents, integrating them with various AWS services to create smart, autonomous systems. You'll also explore the Converse API, enabling more natural, context-aware conversations in your AI applications. Throughout the course, hands-on labs and real-world scenarios will help you apply these concepts. By the course's end, you'll be designing and implementing advanced generative AI applications with Amazon Bedrock.

• Design and implement AI applications with Retrieval-Augmented Generation (RAG) using Amazon Bedrock Knowledge Bases
• Build and configure AI agents using Amazon Bedrock Agents, developing autonomous systems that can understand user intentions, orchestrate multiple tasks, and integrate with AWS services through action groups
• Create sophisticated AI workflows using Prompt Management and Flows, learning to design, test, and deploy complex prompt chains that coordinate multiple AI operations and manage variable inputs effectively
• Implement conversation handling using the Bedrock Converse API, developing applications that maintain context, integrate with external tools, and support multi-step interactions with foundation models
• Apply best practices for building production-ready AI applications, including implementing proper error handling, managing conversation context, optimizing response quality, and ensuring scalable performance