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.

• 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

• Software Developer
• Systems Architect or Engineer with python experience

• AWS Technical Essentials
• Intermediate-level proficiency in Python

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

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

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

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 bias
• Demonstration Image bias mitigation

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 finetuning
• Securing generative AI applications
• Generative AI application architecture

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

7) LangChain

• Optimizing LLM performance
• Using models with LangChain
• Constructing prompts
• Demonstration Bedrock with LangChain using a prompt that includes context
• Structuring documents with indexes
• Storing and retrieving data with memory
• Using chains to sequence components
• Managing external resources with LangChain agents

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
•  Chatbot
•  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