AW330: Developing Generative AI Applications on AWS™

Agenda:

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

Objectives:

In this course AW330 Developing Generative AI Applications on AWS™, 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

Target audience:

This course AW330 Developing Generative AI Applications on AWS™ is intended for:

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

Prerequisites:

To participate in the course AW330 Developing Generative AI Applications on AWS™ at qSkills™, you should have completed the following AWS™ training:

• AW110 AWS™ Technical Essentials

In addition, you should meet the following prerequisites:

•  Intermediate-level proficiency in Python

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