Developing Generative AI Applications on AWS

This course is designed to introduce generative AI to software developers interested in leveraging large language models without fine-tuning. The course provides an overview of generative AI, planning a generative AI project, getting started with Amazon Bedrock, the foundations of prompt engineering, and the architecture patterns to build generative AI applications using Amazon Bedrock and LangChain.Prerequisites:We recommend that attendees of this course have:AWS Technical EssentialsIntermediate-level proficiency in PythonCourse Objectives:Describe generative AI and how it aligns to machine learningDefine the importance of generative AI and explain its potential risks and benefitsIdentify business value from generative AI use casesDiscuss the technical foundations and key terminology for generative AIExplain the steps for planning a generative AI projectIdentify some of the risks and mitigations when using generative AIUnderstand how Amazon Bedrock worksFamiliarize yourself with basic concepts of Amazon BedrockRecognize the benefits of Amazon BedrockList typical use cases for Amazon BedrockDescribe the typical architecture associated with an Amazon Bedrock solutionUnderstand the cost structure of Amazon BedrockImplement a demonstration of Amazon Bedrock in the AWS Management ConsoleDefine prompt engineering and apply general best practices when interacting with FMsIdentify the basic types of prompt techniques, including zero-shot and few-shot learningApply advanced prompt techniques when necessary for your use caseIdentify which prompt-techniques are best-suited for specific modelsIdentify potential prompt misusesAnalyze potential bias in FM responses and design prompts that mitigate that biasIdentify the components of a generative AI application and how to customize a foundation model (FM)Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIsIdentify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applicationsDescribe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon BedrockDescribe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applicationsApply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach