Developing Generative AI Applications on AWS

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

This course is intended for:

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

We recommend that attendees of this course have:
• AWS Technical Essentials
• Intermediate-level proficiency in Python

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 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
• Demonstration: 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
• Demonstration: Bedrock with LangChain Using a Prompt that Includes Context

Module 8: Architecture Patterns

• Introduction to architecture patterns
• Text summarization
• Demonstration: Text Summarization of Small Files with Anthropic Claude
• Demonstration: Abstractive Text Summarization with Amazon Titan Using LangChain
• Question answering
• Demonstration: Using Amazon Bedrock for Question Answering
• Chatbots
• Demonstration: Conversational Interface – Chatbot with AI21 LLM
• Code generation
• Demonstration: Using Amazon Bedrock Models for Code Generation
• LangChain and agents for Amazon Bedrock
• Demonstration: Integrating Amazon Bedrock Models with LangChain Agents