Developing Generative AI Applications on AWS (DGAIA)

Prerequisites

We recommend that attendees of this course have:

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

Who Should Attend

This course is intended for:

• Software developers interested in using LLMs without fine-tuning

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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