AWS Generative AI for Developers

Syllabus

• Week 1

  Introduction to Generative AI - Art of the Possible

  • Introduction to Generative AI

    In this activity you will learn about the relationship between ML and generative AI.

  • Basics of Generative AI

    In this activity you will get an introduction to generative AI and how businesses are deriving business value from the use of generative AI.

  • Generative AI use cases

    In this lesson you will learn about some of the typical use cases for generative AI.

  • Generative AI in Practice

    This activity describes a real-world scenario about how generative AI can benefit customers like you.

  • Risks and Benefits

    This lesson reviews some of the risks and benefits involved in using generative AI in your organisation.

  • Technical Foundations and terminology for generative AI

    The purpose of this activity is to give you a better understanding of how generative AI works. You will cover some key factors such as foundation models (FMs), pretraining, and how transformers play a major role in the process.

  • Planning a Generative AI Project

    In this activity, you will learn about the process of defining the problem and use case based on the immediate and future needs of the business.

  • Risks and Mitigation

    In this activity, you will continue the process of evaluating the use of generative AI for your business needs.

• Week 2

  Getting started with Amazon Bedrock and Prompt Engineering

  • Introduction to Amazon Bedrock

    In this activity, we get started with our understanding of the core functionality of Amazon Bedrock

  • Technical Overview for Amazon Bedrock

    By the end of this activity, you will be able to do the following: Identify the elements of the Amazon Bedrock service architecture. Identify the key service integrations for Amazon Bedrock.

  • Amazon Bedrock Demonstrations

    By the end of this lesson, you will be able to use the AWS Management Console to complete the basic functions of Amazon Bedrock.

  • Basics of Foundation Models

    In the coming weeks, you will learn to create and optimise prompts for a variety of generative artificial intelligence (generative AI) models.

  • Fundamentals of Prompt Engineering

    In this activity we get started with the fundamentals of prompt engineering.

• Week 3

  Designing Effective, Safe, and Model-Specific Prompts with Amazon Bedrock

  • Welcome to the Week

    When crafting and manipulating prompts, there are certain techniques you can use to achieve the response you want from AI models.

  • Prompt Type and Techniques

    In this activity we go through different prompt types and techniques with tree of thoughts techniques.

  • Model-Specific Prompt Techniques

    In this activity you will learn about prompt design and key concepts for three specific models.

  • Improving Prompt Safety and Reducing Bias

    In this activity, you will learn how to spot and fix issues like prompt injection, prompt leaking, and bias in AI. Discover simple ways to make your prompts safer and your data more fair.

  • Introduction to Amazon Bedrock Foundation Models

    Amazon Bedrock offers several natural language processing (NLP) capabilities that can assist data scientists in their work.

• Week 4

  Building Generative AI Applications Using Amazon Bedrock

  • Using Amazon Bedrock FMs for Inference

    Welcome to Week Four! This week we return to Building Generative AI Applications Using Amazon Bedrock.

  • Amazon Bedrock Methods

    Amazon Bedrock provides a list of APIs you can access in your respective notebooks and AWS Lambda functions to access Amazon Bedrock.

  • Application components

    Overview of Generative AI Application Components

  • Using LangChain

    This activity will focus on Optimising LLM Performance using LangChain

  • Langchain Components

    In this section, you will learn about the language models that are used in LangChain. These models include LLMs, chat models, and text embedding models.

  • Demonstration

    In this demo activity, you will watch a demonstration of running the notebooks that support the generative AI use cases discussed in the earlier activities.

• Week 5

  Building and Evaluating RAG Applications with Amazon Bedrock Knowledge Bases

  • Using Knowledge Bases

    Discover RAG applications and use cases, explore its architecture, tackle RAG development challenges, learn about Amazon Bedrock Knowledge Bases, and uncover best practices and advanced techniques for effective RAG implementation.

  • Amazon Bedrock Knowledge Bases

    This activity will cover: Amazon Bedrock Knowledge Bases. Data Ingestion into Knowledge Bases.

  • Fully Managed RAG using Amazon Bedrock Services

    This activity will cover: Fully Managed RAG using Amazon Bedrock Knowledge Bases. Customised RAG using Retrieve.

  • Advanced RAG

    In this activity you will cover: Evaluating RAG Applications. Best Practices and Advanced Techniques for RAG.

  • Demo 2: Build and Evaluate RAG Applications Using Amazon Bedrock Knowledge Bases

    Build and Evaluate RAG Applications Using Amazon Bedrock Knowledge Bases.

• Week 6

  Building and Using Intelligent Agents with Amazon Bedrock

  • Using Agents

    In this activity we will cover the following topics: Creating Amazon Bedrock Agents. Agent Action Groups. Testing and Invoking Agents.

  • Creating Amazon Bedrock Templates

    In this activity we will cover: Creating and Deplaying Agents. Creating and Deploying Agents in the AWS Console. Creating and Deploying Agents Programmatically.

  • Agent Agent Groups

    In this activity you will learn: Creating Agent Action Groups. Action Group Invocation Types. Quick create Lambda function. Implementing return control. Amazon Bedrock Agents Integrations. Integrating Amazon Bedrock Guardrails.

  • Testing and Invoking Agents

    In this activity you will cover the following topics: Deploying an agent. Invoking an agent. Additional agent functionality. Knowledge Check.

  • Demo 3: Explore Amazon Bedrock Agents Integrated with Amazon Bedrock Knowledge Bases and Guardrails

    Explore Amazon Bedrock Agents Integrated with Amazon Bedrock Knowledge Bases and Guardrails.

• Week 7

  Getting Started with Amazon Q Developer

  • Amazon Q Developer Getting Started

    Learn to set up a dev environment for Amazon Q Developer, interact with it in VS Code, transform Java 8 code to Java 17, and use it to implement new features in your project.

  • Architecture and Use Cases

    In this activity, you will learn: The technical concepts of Amazon Q Developer. Typical use cases for Amazon Q Developer. The requirements to implement Amazon Q Developer in a real-world scenario.

  • Using Amazon Q Developer

    Learn to install the Amazon Q extension in VS Code, set up an AWS Builder ID, interact with Amazon Q Developer, upgrade Java 8 code to Java 17, and request new feature implementation in your project.