Developing Generative AI Applications on AWS

Course Information

Course Name

AWS-GAA: Developing Generative AI Applications on AWS

Duration

2 Days

Certification

Private: AWS Certified Machine Learning – Specialty

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

Audience Profile

This course is intended for:

• Software developers interested in leveraging large language models without fine-tuning

• Duration: 2 days

Prerequisites

We recommend that attendees of this course have:

• AWS Technical Essentials

• Intermediate-level proficiency in Python

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

Day 1 :

Module 1: Introduction to Generative AI – Art of the Possible

• Overview of ML

• Basics of generative AI

• Generative AI use cases

• Generative AI in practice

• Risks and benefits

Module 2: Planning a Generative AI Project

• Generative AI fundamentals

• Generative AI in practice

• Generative AI context

• Steps in planning a generative AI project

• Risks and mitigation

Module 3: Getting Started with Amazon Bedrock

• Introduction to Amazon Bedrock

• Architecture and use cases

• How to use Amazon Bedrock

• Demonstration: Setting Up Amazon Bedrock Access and Using Playgrounds

Module 4: Foundations of Prompt Engineering

• Basics of foundation models

• Fundamentals of prompt engineering

• Basic prompt techniques

• Advanced prompt techniques

• Demonstration: Fine-Tuning a Basic Text Prompt

• Model-specific prompt techniques

• Addressing prompt misuses

• Mitigating bias

• Demonstration: Image Bias-Mitigation

Day 2 :

Module 5: Amazon Bedrock Application Components

• Applications and use cases

• Overview of generative AI application components

• Foundation models and the FM interface

• Working with datasets and embeddings

• Demonstration: Word Embeddings

• Additional application components

• RAG

• Model fine-tuning

• Securing generative AI applications

• Generative AI application architecture

Module 6: Amazon Bedrock Foundation Models

• Introduction to Amazon Bedrock foundation models

• Using Amazon Bedrock FMs for inference

• Amazon Bedrock methods

• Data protection and auditability

• Lab: Invoke Amazon Bedrock model for text generation using zero-shot prompt

Module 7: LangChain

• Optimizing LLM performance

• Integrating AWS and LangChain

• Using models with LangChain

• Constructing prompts

• Structuring documents with indexes

• Storing and retrieving data with memory

• Using chains to sequence components

• Managing external resources with LangChain agents

Module 8: Architecture Patterns

• Introduction to architecture patterns

• Text summarization

• Lab: Using Amazon Titan Text Premier to summarize text of small files

• Lab: Summarize long texts with Amazon Titan

• Question answering

• Lab: Using Amazon Bedrock for question answering

• Chatbots

• Lab: Build a chatbot

• Code generation

• Lab: Using Amazon Bedrock Models for Code Generation

• LangChain and agents for Amazon Bedrock

• Lab: Building conversational applications with the Converse API

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