What this book covers
Chapter 1, Exploring Amazon Bedrock, provides an introduction to Amazon Bedrock, starting with exploring the Generative AI landscape, foundation models offered by Amazon Bedrock, guidelines for selecting the right model, additional Generative AI capabilities, and potential use cases.
Chapter 2, Accessing and Utilizing Models in Amazon Bedrock, provides different ways to access and utilize Amazon Bedrock and its capabilities, covering different interfaces, core APIs, code snippets, Bedrock’s integration with LangChain to build customized pipelines, chaining multiple models, and insights into Amazon Bedrock’s playground called PartyRock.
Chapter 3, Engineering Prompts for Effective Model Usage, explores the art of prompt engineering, its various techniques, and best practices for crafting effective prompts to harness the power of Generative AI models on Amazon Bedrock. It equips you with a comprehensive understanding of prompt engineering principles, enabling you to design prompts that elicit desired outcomes from Bedrock’s models.
Chapter 4, Customizing Models for Enhanced Performance, provides a comprehensive guide on customizing foundation models using Amazon Bedrock to enhance their performance for domain-specific use cases. It covers the rationale behind model customization, data preparation techniques, the process of creating custom models, analyzing results, and best practices for successful model customization.
Chapter 5, Harnessing the Power of RAG, explores the Retrieval Augmented Generation (RAG) approach, which enhances language models by incorporating external data sources to mitigate hallucination issues. It dives into the integration of RAG with Amazon Bedrock, including the implementation of knowledge bases, and provides hands-on examples of using RAG APIs and real-world scenarios. Additionally, the chapter covers alternative methods for implementing RAG, such as using LangChain orchestration and other Generative AI systems, and discusses the current limitations and future research directions with Amazon Bedrock in the context of RAG.
Chapter 6, Generating and Summarizing Text with Amazon Bedrock, dives into the architecture patterns, where you will learn how to leverage Amazon Bedrock’s capabilities for generating high-quality text content and summarizing lengthy documents, and explores various real-world use cases.
Chapter 7, Building Question Answering Systems and Conversational Interfaces, covers architectural patterns for question answering on small and large documents, conversation memory, embeddings, prompt engineering techniques, and contextual awareness techniques to build intelligent and engaging chatbots and question-answering systems.
Chapter 8, Extracting Entities and Generating Code with Amazon Bedrock, explores the applications of entity extraction across various domains, provides insights into implementing it using Amazon Bedrock, and investigates the underlying principles and methodologies behind Generative AI for code generation, empowering developers to streamline their workflows and enhance productivity.
Chapter 9, Generating and Transforming Images Using Amazon Bedrock, dives into the world of image generation using Generative AI models available on Amazon Bedrock. It explores real-world applications of image generation, multimodal models available within Amazon Bedrock, design patterns for multimodal systems, and ethical considerations and safeguards provided by Amazon Bedrock.
Chapter 10, Developing Intelligent Agents with Amazon Bedrock, provides you with a comprehensive understanding of agents, their benefits, and how to leverage tools such as LangChain to build and deploy agents tailored for Amazon Bedrock, enabling you to harness the power of Generative AI in real-world industrial use cases.
Chapter 11, Evaluating and Monitoring Models with Amazon Bedrock, provides guidance on how to effectively evaluate and monitor the Generative AI models of Amazon Bedrock. It covers automatic and human evaluation methods, open source tools for model evaluation, and leveraging services such as CloudWatch, CloudTrail, and EventBridge for real-time monitoring, auditing, and automation of the Generative AI lifecycle.
Chapter 12, Ensuring Security and Privacy in Amazon Bedrock, explores robust security and privacy measures implemented by Amazon Bedrock, ensuring the protection of your data and enabling responsible AI practices. It covers topics such as data localization, isolation, encryption, access control through AWS Identity and Access Management (IAM), and the implementation of guardrails for content filtering and safeguarding against misuse and aligning with safe and responsible AI policies.
