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Generative AI Applications with Amazon Bedrock

Morgan Willis, Russell Sayers, and Alex G.

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.

Please note: The hands-on exercises are optional and require access to your own AWS account. Completing these activities may result in minimal usage charges.

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What's inside

Syllabus

Module 1: Knowledge Bases and Workflows

In this module, you will explore how to improve generative AI responses using Amazon Bedrock Knowledge Bases with retrieval-augmented generation (RAG). You will learn to parse and chunk content, integrate document storage, and use APIs to query structured knowledge. The module also introduces prompt management and prompt flows, enabling you to design dynamic AI workflows. Through labs and demos, you will build applications that ask questions, process responses, and chain prompts in real-world use cases.

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Career center

Learners who complete Generative AI Applications with Amazon Bedrock will develop knowledge and skills that may be useful to these careers:

Generative AI Developer

A Generative AI Developer crafts innovative applications leveraging large language models and other generative AI techniques. This course directly aids in this role by deeply exploring Generative AI Applications with Amazon Bedrock. Learners gain practical experience in building and deploying solutions, particularly mastering Amazon Bedrock Knowledge Bases for context-aware interactions and Prompt Management for intricate AI workflows. The hands-on labs teach how to integrate document storage and use APIs for structured knowledge, enabling effective development of advanced generative AI applications. This foundational understanding is crucial for creating sophisticated AI behaviors and autonomous systems.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys intelligent systems by applying machine learning principles. This course helps build a foundation for aspiring Machine Learning Engineers focusing on the application layer of generative AI. By mastering Generative AI Applications with Amazon Bedrock, individuals learn to use powerful tools like Amazon Bedrock Agents for task automation and the Converse API for natural conversations. The curriculum, covering RAG and prompt flows, also prepares learners to integrate AI solutions with various AWS services, which is a common requirement in this field for deploying scalable and robust machine learning applications.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer focuses on developing and implementing AI systems and applications. This course is an excellent pathway for those aiming to excel as an Artificial Intelligence Engineer, providing hands-on expertise in Generative AI Applications with Amazon Bedrock. It covers critical areas such as enhancing AI responses using Retrieval-Augmented Generation with Amazon Bedrock Knowledge Bases and designing dynamic AI workflows through prompt management and flows. Furthermore, the course's emphasis on building AI-driven agents for task automation, integrating them with AWS services, directly prepares learners to create smart, autonomous systems crucial for advanced AI engineering roles.

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Prompt Engineer

A Prompt Engineer specializes in crafting, refining, and optimizing prompts to elicit desired behaviors and responses from large language models. This course is particularly tailored for aspiring Prompt Engineers, as it dedicates significant attention to Amazon Bedrock Prompt Management and Flows. Learners will gain expertise in designing intricate workflows that chain multiple AI operations, a core skill for maximizing AI performance. The practical approach to asking questions, processing responses, and chaining prompts in real-world use cases, combined with discussions on the Converse API for context-aware conversations, provides invaluable experience for excelling in this specialized and evolving field.

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AI Solutions Architect

An AI Solutions Architect designs and plans complex AI systems, ensuring they meet business requirements and integrate effectively within existing infrastructure. This course helps build a foundation for an aspiring AI Solutions Architect by focusing on Generative AI Applications with Amazon Bedrock. Understanding how to leverage Amazon Bedrock Knowledge Bases for RAG, implementing prompt management, and deploying AI-driven agents for task automation are critical for designing robust solutions. The curriculum's exploration of integrating agents with various AWS services helps learners envision and architect scalable, domain-specific AI applications, which is essential for this role.

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Conversational AI Developer

A Conversational AI Developer builds systems that enable natural, human-like interactions, such as chatbots and virtual assistants. This course is highly relevant for those pursuing a career as a Conversational AI Developer, offering direct experience with Generative AI Applications with Amazon Bedrock. A key focus is the Converse API, which allows for more natural and context-aware conversations. Learners will also understand how to create conversational flows using Amazon Bedrock Agents, configuring them with actions and session memory to build interactive applications like booking systems. This practical knowledge is indispensable for designing sophisticated, user-centric conversational experiences.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. For a Software Engineer interested in specializing in cutting-edge AI, this course offers a distinct advantage by focusing on Generative AI Applications with Amazon Bedrock. Learners gain practical skills in building complex software solutions that incorporate generative AI, including integrating Knowledge Bases for enhanced AI responses and utilizing Prompt Management for dynamic AI workflows. The hands-on experience in building agent-powered applications and integrating them with diverse AWS services directly equips individuals with the tools to engineer next-generation, AI-driven software systems.

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Technical Consultant Cloud Artificial Intelligence

A Technical Consultant Cloud Artificial Intelligence advises clients on designing, migrating, and optimizing AI solutions on cloud platforms. This course is highly beneficial for an aspiring Technical Consultant Cloud Artificial Intelligence, providing specialized knowledge in Generative AI Applications with Amazon Bedrock. The practical insights into Amazon Bedrock Knowledge Bases, Prompt Management and Flows, and AI-driven agents equip consultants to propose and implement cutting-edge generative AI solutions. Understanding how to integrate these components with AWS services for real-world scenarios enables consultants to guide clients effectively in leveraging Amazon Bedrock for their specific business needs, ensuring successful cloud AI adoption.

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Applied Scientist Artificial Intelligence

An Applied Scientist Artificial Intelligence often bridges research and engineering, developing practical AI solutions and prototypes. This course can be highly valuable for an Applied Scientist Artificial Intelligence, offering a direct pathway to implementing generative AI concepts using Amazon Bedrock. While this role often typically requires an advanced degree, the course's emphasis on building generative AI applications, including leveraging Knowledge Bases for RAG and deploying AI-driven agents for task automation, provides practical, hands-on experience crucial for translating theoretical knowledge into tangible products. Learners will acquire specific skills in designing dynamic AI workflows, which is essential for innovative application development.

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Technical Product Manager Artificial Intelligence

A Technical Product Manager Artificial Intelligence guides the development of AI products from conception to launch, balancing technical feasibility with market needs. This course provides invaluable insight for a Technical Product Manager Artificial Intelligence by offering a deep dive into Generative AI Applications with Amazon Bedrock. Understanding the capabilities of Amazon Bedrock Knowledge Bases for RAG, Prompt Management and Flows, and AI-driven agents equips product managers to define innovative features and set realistic roadmaps. The hands-on labs allow for a practical grasp of what is achievable with current generative AI technologies, crucial for making informed product decisions and communicating effectively with engineering teams.

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Automation Engineer

An Automation Engineer designs and implements systems to automate various processes, often using software and robotics. This course helps an Automation Engineer looking to integrate advanced AI capabilities into their automation solutions. The second module, specifically focusing on AI-driven agents for task automation with Amazon Bedrock, directly aligns with this career path. Learning to configure and deploy Bedrock Agents, integrating them with various AWS services to create smart, autonomous systems, provides cutting-edge tools. The hands-on labs for building agent-powered apps, such as booking systems and workflow automation solutions, offer practical experience for enhancing automation efforts.

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Machine Learning Operations Engineer

A Machine Learning Operations Engineer focuses on the deployment, monitoring, and maintenance of machine learning models and applications in production environments. This course helps a Machine Learning Operations Engineer due to its focus on building and deploying Generative AI Applications with Amazon Bedrock. Understanding how components like Knowledge Bases, Prompt Management, and AI-driven agents function within the AWS ecosystem is crucial for operationalizing generative AI. The hands-on experience with integrating these systems with various AWS services helps in designing robust MLOps pipelines for deployment, scaling, and continuous improvement of generative AI-powered applications.

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Cloud Engineer

A Cloud Engineer designs, implements, and manages cloud-based infrastructure and services. This course helps a Cloud Engineer, particularly one specializing in integrating advanced AI capabilities into cloud environments. By focusing on Generative AI Applications with Amazon Bedrock, learners gain practical experience with a specific AWS service. Understanding how to deploy and manage components like Knowledge Bases, Prompt Management, and AI-driven agents, which often rely on underlying AWS services like Lambda functions, can help build a foundation in deploying comprehensive generative AI solutions within the AWS ecosystem, expanding their cloud expertise.

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DevOps Engineer

A DevOps Engineer streamlines software development and deployment processes, ensuring reliability and efficiency. This course may be helpful for a DevOps Engineer interested in the operational aspects of deploying and managing generative AI applications on the AWS platform. Understanding Generative AI Applications with Amazon Bedrock provides insight into the architecture and components of these advanced systems, such as Bedrock Agents and their integration with other AWS services via Lambda functions. This knowledge can facilitate creating robust automation pipelines for deployment, monitoring, and scaling of generative AI solutions, enhancing overall system reliability and performance.

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Data Scientist

A Data Scientist extracts insights from complex data, often building predictive models. While the core focus of this course is on building applications rather than statistical modeling or deep data analysis, it may be useful for a Data Scientist who wishes to understand the practical deployment and application of generative AI. By learning Generative AI Applications with Amazon Bedrock, including RAG and AI agents, data scientists can better leverage pre-trained models and integrate them into solutions that provide context-aware interactions or automate tasks. This understanding helps bridge the gap between model development and real-world AI application.

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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Generative AI Applications with Amazon Bedrock.

AWS Certified Developer Associate Practice Tests

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Study guide for the AWS Certified Bedrock Developer Associate exam. It covers all the topics included in the exam and provides practice questions and mock exams. It valuable resource for anyone who wants to prepare for this certification.

AWS Certified Developer Associate Practice Tests:...

Paperback

Prompt Engineering for Generative AI

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Provides a comprehensive guide to prompt engineering, covering techniques for crafting effective inputs to generative AI models. It's particularly useful for understanding how to obtain reliable and predictable results, which is crucial for both beginners and those looking to deepen their practical skills. This book is valuable as a current reference for anyone working with generative AI.

Generative AI Applications with Amazon Bedrock

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What's inside

Syllabus

Save this course

Career center

Reading list

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