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    Developing Generative AI Applications on AWS (DGAIA)

    Cloud Computing - AWS
    This course is designed to introduce generative artificial intelligence (AI) to software developers interested in using large language models (LLMs) 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.

    Developing Generative AI Applications on AWS (DGAIA) Objectives

    • 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 foundation models (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 an 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 LLMs prompt templates chains chat models text embeddings models document loaders retrievers and Agents for Amazon Bedrock
    • Describe architecture patterns that you can implement with Amazon Bedrock for building generative AI applications
    • Apply the concepts to build and test sample use cases that use the various Amazon Bedrock models LangChain and the Retrieval Augmented Generation (RAG) approach

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    Key Point of Training Programs

    • Developing Generative AI Applications on AWS (DGAIA) Prerequisites

      ho should attend
      This course is intended for:

      Software developers interested in using LLMs without fine-tuning
      Prerequisites
      We recommend that attendees of this course have:

      Completed AWS Technical Essentials (AWSE)
      Intermediate-level proficiency in Python

    • Developing Generative AI Applications on AWS (DGAIA) Delivery Format

      In-Person

      Online

    • Developing Generative AI Applications on AWS (DGAIA) 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 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
      Model-specific prompt techniques
      Demonstration: Fine-tuning a basic text prompt
      Addressing prompt misuses
      Mitigating bias
      Demonstration: Image bias mitigation
      Day 2

      Module 5: Amazon Bedrock Application Components
      Overview of generative AI application components
      Foundation models and the FM interface
      Working with datasets and embeddings
      Demonstration: Word embeddings
      Additional application components
      Retrieval Augmented Generation (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 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
      Chatbot
      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