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Description

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Overview:

This course is designed to introduce generative AI to software developers interested in leveraging large language models 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.

Prerequisites:

We recommend that attendees of this course have:

  • AWS Technical Essentials
  • Intermediate-level proficiency in Python

Audience:

This course is intended for:

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

Course Objectives:

  • 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

Course Outline:

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

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

Additional information

Length

2 days

Guaranteed to run

No