719-298-4047info@force7training.com
Student Login
Force7 Training
FRCAWS-17AWS

Generative AI Essentials on AWS

Duration · 1 dayVirtual + In-PersonInstructor-Led

$650

Course Description

This one-day instructor-led course introduces learners to foundational generative AI concepts and how to build, deploy, and use generative AI solutions using services from Amazon Web Services. Participants explore core AI terminology, large language models (LLMs), prompt engineering, responsible AI practices, and key AWS generative AI services including Amazon Bedrock and Amazon Q.

— Be First in Line —

Register Your Interest

We're finalizing the schedule for Generative AI Essentials on AWS. Add your details below and we'll notify you the moment a session opens for registration — no payment or commitment required.

Audience Profile

This course is intended for:

  • IT professionals
  • Business analysts
  • Cloud practitioners
  • Developers
  • Technical managers
  • Solution architects
  • AI enthusiasts

Prerequisites

Before enrolling, you should have:

  • Basic understanding of cloud computing
  • General technical knowledge recommended
  • No prior AI or machine learning experience required

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Define generative AI and identify common use cases
  • 2Explain foundational AI and machine learning concepts
  • 3Describe large language models and foundation models
  • 4Understand prompt engineering fundamentals
  • 5Identify core AWS generative AI services
  • 6Use Amazon Bedrock for foundation model access
  • 7Describe Amazon Q capabilities and use cases
  • 8Recognize responsible AI and security considerations
  • 9Explore business applications for generative AI on AWS

— Day-by-Day —

Course Outline

Module 1

Introduction to Generative AI

Topics

  • What is artificial intelligence?
  • Machine learning vs. generative AI
  • Evolution of generative AI
  • Common generative AI use cases
  • Business value and industry adoption
  • Understanding foundation models and LLMs
  • Lab / Demonstration
  • Exploring real-world generative AI examples

Module 2

Generative AI Fundamentals

Topics

  • How generative AI models work
  • Tokens, embeddings, and transformers
  • Training vs. inference
  • Model parameters and context windows
  • Hallucinations and model limitations
  • Retrieval-Augmented Generation (RAG) overview
  • Lab / Demonstration
  • Generating text with a foundation model

Module 3

Prompt Engineering Essentials

Topics

  • Prompt design fundamentals
  • Zero-shot, one-shot, and few-shot prompting
  • Prompt structure and context
  • Controlling model outputs
  • Prompt optimization techniques
  • Prompt engineering best practices
  • Hands-On Exercise
  • Creating effective prompts for business scenarios

Module 4

AWS Generative AI Services Overview

Topics

  • Introduction to generative AI services on AWS
  • Overview of Amazon Bedrock
  • Foundation model providers available in Amazon Bedrock
  • Introduction to Amazon Q
  • Overview of AWS AI/ML ecosystem
  • Security and compliance considerations
  • Demonstration
  • Navigating AWS generative AI services

Module 5

Building with Amazon Bedrock

Topics

  • Accessing foundation models in Amazon Bedrock
  • Model selection considerations
  • Text generation workflows
  • Knowledge bases and agents overview
  • Integrating generative AI applications
  • Monitoring and cost considerations

Hands-On Lab

  • Using Amazon Bedrock to generate responses and summaries

Module 6

Responsible AI and Governance

Topics

  • Responsible AI principles
  • Bias and fairness considerations
  • Data privacy and security
  • Human oversight and validation
  • Governance and compliance strategies
  • Risk management for generative AI systems
  • Group Discussion
  • Evaluating ethical AI scenarios

Module 7

Business Use Cases and Adoption

Topics

  • Generative AI for productivity
  • Customer service and chatbots
  • Content generation and summarization
  • Software development assistance
  • Knowledge management solutions
  • Planning generative AI adoption strategies
  • Activity
  • Identifying generative AI opportunities within an organization
  • Course Wrap-Up
  • Review and Q&A
  • Key concept review
  • AWS generative AI service recap
  • Best practices summary
  • Additional learning paths and certifications
  • Final Q&A session
  • Suggested Hands-On Labs
  • Exploring foundation model outputs
  • Prompt engineering workshop
  • Amazon Bedrock text generation lab
  • AI-powered summarization exercise
  • Responsible AI evaluation activity

— Next Steps —

Recommended Follow-On Courses

Continue your learning path with courses that build on what you just covered.

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

— Keep Exploring —

Need a different angle?

Browse the full AWS catalog or chat with an advisor about a custom training plan for your team.