Developing Generative AI Solutions on AWS
Course 1244
2 DAY COURSE

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 Solutions on AWS Benefits

• Training Prerequisites

  • AWS Technical Essentials
  • Intermediate-level proficiency in Python 

Gen AI Solutions on AWS Training Outline

Learning Objectives

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 Finetuning a basic text prompt
• Addressing prompt misuses
• Mitigating perspective awareness
• Demonstration: Image perspective awareness mitigation

Module 5: Amazon Bedrock Application Components

• Overview of generative AI application components
• Applications and use cases
• Foundation models and the FM interface
• Working with datasets and embeddings
• Demonstration: Word embeddings
• Additional application components
• Retrieval Augmented Career Stages 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 Amazon Bedrock model for text Career Stages 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
• Question answering
• Demonstration Using Amazon Bedrock for question-answering
• Chatbot
• Lab: Build a chatbot • Code Career Stages
• Demonstration Using Amazon Bedrock models for code Career Stages
• LangChain and agents for Amazon Bedrock
• Lab: Building conversational applications with the Converse API