Developing Generative AI Applications on AWS

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.


In this course, you will:

  • 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

This course is intended for:

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

We recommend that attendees of this course have:

  • AWS Technical Essentials
  • Intermediate-level proficiency in Python

Course outline

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

Demonstration: Fine-Tuning a Basic Text Prompt

Model-specific prompt techniques

Addressing prompt misuses

Mitigating bias

Demonstration: Image Bias-Mitigation

Day 2

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


Model fine-tuning

Securing generative AI applications

Generative AI application architectureDeveloping Generative AI Applications on AWS AWS Classroom Training

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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


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

Why choose Cloud Wizard
  • Advanced Tier Training Partner
  • Amazon Authorised Instructors
  • Official AWS Content
  • Hands-on Labs
Class Deliverables
  • E-Content kit by AWS
  • Hands-on labs
  • Class completion certificates
  • Exam Prep sessions

Dates Available - Click on Book Now to proceed

Virtual2 daysAll DayMay 9, 202435,000
Virtual2 daysAll DayMay 23, 202435,000
Virtual2 daysAll DayJune 3, 202435,000
Virtual2 daysAll DayJune 17, 202435,000
Virtual2 daysAll DayJuly 1, 202435,000
Virtual2 daysAll DayJuly 15, 202435,000

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To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.

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There is no minimum number of candidates required, we are happy to train 1 to 1 . With regards to the maximum number, we can accomodate 30 learners in one batch.

  1. Training Delivered by an Amazon Authorized Instructor.
  2. AWS Content E-Kit
  3. Hands-on-labs for 30 days
  4. Class attendance certificate

You will get the access to course content & lab on first day of your training session.

The course Completion Certificate will be issued to your email id within 2 weeks of completing your course.

A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day)




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