Developing Generative AI Applications on AWS

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 architectureDeveloping Generative AI Applications on AWS AWS Classroom Training

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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