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Training
Developing Generative AI Applications on AWS
Course Information
Course Name
Developing Generative AI Applications on AWS
Duration
2 Days
Certification
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
Overview
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.
- Course level: Advanced
- Duration: 2 days
Overview
Overview
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.
- Course level: Advanced
- Duration: 2 days
Audience Profile
Audience Profile
This course is intended for:
- Software developers interested in leveraging large language models without fine-tuning
Prerequisities
Prerequisites
We recommend that attendees of this course have:
- AWS Technical Essentials
- Intermediate-level proficiency in Python
At Course Completion
At Course Completion
In this course, you will learn to:
- Describe generative AI and how it aligns to machine learning
- Define the importance of generative AI and explain its potential risks and benefits
- Identify business value from generative AI use cases
- Discuss the technical foundations and key terminology for generative AI
- Explain the steps for planning a generative AI project
- Identify some of the risks and mitigations when using generative AI
- Understand how Amazon Bedrock works
- Familiarize yourself with basic concepts of Amazon Bedrock
- Recognize the benefits of Amazon Bedrock
- List typical use cases for Amazon Bedrock
- Describe the typical architecture associated with an Amazon Bedrock solution
- Understand the cost structure of Amazon Bedrock
- Implement a demonstration of Amazon Bedrock in the AWS Management Console
- Define prompt engineering and apply general best practices when interacting with FMs
- Identify the basic types of prompt techniques, including zero-shot and few-shot learning
- Apply advanced prompt techniques when necessary for your use case
- Identify which prompt-techniques are best-suited for specific models
- Identify potential prompt misuses
- Analyze potential bias in FM responses and design prompts that mitigate that bias
- Identify 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 APIs
- Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications
- Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock
- Describe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applications
- Apply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach
Course Outline
Course Outline
Module 1: Exploring Components of Generative AI Applications on AWS
- Understanding generative AI concepts
- Identifying AWS generative AI stack components
- Designing generative AI application components
Module 2: Programming with Amazon Bedrock
- Guiding model response generation
- Using Amazon Bedrock programmatically
- Steps in planning a generative AI project
- Risks and mitigation
Hands-on lab: Develop with Amazon Bedrock APIs
Hands-on lab: Develop Streaming Patterns with Amazon Bedrock APIs
Module 3: Applying Prompt Engineering for Developers
- Introducing prompt engineering
- Introducing prompt techniques
- Optimizing prompts for better results
Module 4: Using Amazon Bedrock APIs in Common Architectures
- Implementing architecture patterns with Amazon Bedrock APIs
- Exploring common use cases
- Adding conversational memory to extend context
Hands-on lab: Develop Conversation Patterns with Amazon Bedrock APIs
Advanced prompt techniques.
Module 5: Customizing Generative AI Responses with RAG
- Implementing Retrieval Augmented Generation (RAG)
- Using Amazon Bedrock Knowledge Bases
Hands-on lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases
Module 6: Integrating Open-Source Frameworks with Amazon Bedrock
- Invoking a foundation model in Amazon Bedrock using LangChain
- Using LangChain for context-aware responses
Hands-on lab: Develop a Generative AI Application Pattern using Open-Source Frameworks and
Amazon Bedrock Knowledge Bases
Module 7: Evaluating Generative AI Application Components
- Evaluating application components
- Evaluating model output
- Evaluating RAG output
- Optimizing latency and cost
Hands-on lab: Evaluating Retrieval Augmented Generation (RAG) Applications
Module 8: Implementing Responsible AI
- Understanding responsible AI
- Mitigating bias and addressing prompt misuses
- Using Amazon Bedrock Guardrails
- Hands-on lab: Securing Generative AI Applications Using Bedrock Guardrails
Module 9: Using Tools and Agents in Generative AI Applications
- Using tools
- Understanding AI agents
- Understanding open source agentic frameworks
- Understanding agent interoperability
Module 10: Developing Amazon Bedrock Agents
- Implementing Amazon Bedrock Flows
- Designing Amazon Bedrock Agents
- Developing Amazon Bedrock Inline Agents
- Designing multi-agent collaboration
- Using Amazon Bedrock AgentCore
Hands-on lab: Developing Amazon Bedrock Agents Integrated with Amazon Bedrock Knowledge
Bases and Guardrails
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