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Developing Generative AI Applications on AWS

Length 2 days

Course overview

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Why study this course

This two-day 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.

This course includes presentations, demonstrations, and group exercises.

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What you’ll learn

This course is designed to teach participants how to:

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.
Familiarise yourself with basic concepts of Amazon Bedrock.
Recognise 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.
Analyse potential bias in FM responses and design prompts that mitigate that bias.
Identify the components of a generative AI application and how to customise 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.

AWS at Lumify Work

Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.

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Who is the course for?

This course is intended for:

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

Course subjects

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

Optimising 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 summarisation
Demonstration: Text Summarisation of Small Files with Anthropic Claude
Demonstration: Abstractive Text Summarisation 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

Please note: This is an emerging technology course. Course outline is subject to change as needed.

Prerequisites

We recommend that attendees of this course have:

Completed AWS Technical Essentials
Intermediate-level proficiency in Python

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.