AWS
Generative AI & Prompt Engineering on AWS
The Generative AI & Prompt Engineering on AWS course teaches you how to build and deploy AI applications using AWS services like Bedrock and SageMaker. It focuses on mastering prompt design, model customization, and creating real-world generative AI solutions.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 Weeks
What is Generative AI & Prompt Engineering on AWS?
With the Generative AI & Prompt Engineering on AWS course, you can learn how to make smart AI apps using services like Bedrock and SageMaker.
It focuses on making good prompts, improving models, and putting AI solutions into use that can grow.
You will learn how to make chatbots, automate content, and build AI-powered pipelines.
This course gives you the skills you need to use generative AI in real-world business situations.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
Generative AI & Prompt Engineering on AWS Course Curriculum
.
Introduction to Generative AI
Section 1
Learn the fundamentals of generative AI, foundation models, and AWS ecosystem support.
Generative AI concepts
Foundation models overview
AWS AI/ML landscape
GenAI use cases
.
Amazon Bedrock Overview
Section 2
Understand how to build and scale applications using Bedrock without managing infrastructure.
Bedrock architecture
Model providers (Anthropic, Meta, Stability AI, etc.)
Bedrock APIs & SDKs
Use case demonstrations
.
Prompt Engineering Fundamentals
Section 3
Master effective prompt design to optimize outputs from foundation models.
Prompt structures & templates
Zero-shot & few-shot prompting
Role & context-based prompts
Iterative refinement techniques
.
Advanced Prompt Engineering Techniques
Section 4
Apply advanced strategies to handle complex use cases.
Chain-of-thought prompting
Multi-step reasoning
Guardrails & constraints
Response evaluation
.
Data Preparation & Fine-Tuning
Section 5
Prepare custom datasets and fine-tune models for domain-specific tasks.
Data cleaning & preprocessing
Instruction fine-tuning
Embeddings & vector databases
Custom model deployment
.
AWS ML Services Integration
Section 6
Integrate generative AI with existing AWS ML services for end-to-end workflows.
Amazon SageMaker
AWS Lambda for orchestration
Step Functions workflows
API Gateway integration
.
Building GenAI Applications
Section 7
Design real-world applications leveraging GenAI capabilities.
Chatbots & virtual assistants
Text summarization & Q&A
Image & video generation
Multi-modal applications
.
Security, Governance & Responsible AI
Section 8
Ensure ethical, secure, and compliant use of generative AI.
Content filtering & guardrails
Responsible AI principles
IAM roles & access policies
Data privacy & compliance
.
Performance Optimization & Cost Management
Section 9
Optimize GenAI workloads for efficiency and cost-effectiveness.
Model selection trade-offs
Caching & batching strategies
Resource scaling
Cost monitoring & budgets
.
Hands-On Labs & Exam Preparation
Section 10
Reinforce knowledge through labs, case studies, and practice assessments.
Hands-on Bedrock projects
Prompt engineering labs
End-to-end case studies
Mock exam & best practices
Key Projects
Generative AI & Prompt Engineering on AWS
AI-Powered Customer Support Chatbot
With Amazon Bedrock and Lex, you can make a smart chatbot that can have conversations with multiple turns, answer questions from customers, and give them personalized help. Use prompt engineering to improve responses, connect with backend systems, and make it a scalable service on AWS.
Automated Content Generation Pipeline
Set up a pipeline with SageMaker and Bedrock so that blogs, product descriptions, or marketing copy are made automatically. Use prompt engineering methods to make content more accurate and stylish, and store the outputs in Amazon S3 so they are easy to find and keep track of.
Prompt Optimization & Evaluation Tool
Create a tool that lets you compare the outputs of different foundation models on Amazon Bedrock using different prompt strategies. Implement evaluation metrics, automate testing with Lambda, and visualize results in QuickSight to identify the most effective prompts for specific tasks.
Generative AI & Prompt Engineering on AWS – Associate Training Program
Category
Associate
Exam Name:
Generative AI & Prompt Engineering on AWS – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
What is Generative AI on AWS?
Generative AI on AWS refers to building applications that can create content—text, images, code—by learning from existing data patterns. AWS provides managed services like Bedrock, SageMaker, and Q to build and scale these applications. Techpratham explains that it allows developers to focus on AI logic without managing infrastructure.
What is prompt engineering?
Prompt engineering is the practice of designing inputs to guide AI models toward producing desired outputs. It involves clear instructions, context, and iterative refinement. Techpratham notes that effective prompts drastically improve AI output quality and relevance.
Who should learn Generative AI & Prompt Engineering on AWS?
This is ideal for ML engineers, AI developers, data scientists, and cloud developers who want to integrate AI into applications. Techpratham emphasizes that even software developers can leverage these skills to enhance automation, content generation, or analytics workflows.
What AWS services are used for Generative AI?
Key services include Amazon Bedrock for foundation models, SageMaker for training and deployment, and Amazon Q for LLM-powered workflows. Techpratham highlights that choosing the right service depends on whether you’re prototyping, deploying, or building interactive AI apps.
Are there prerequisites for learning this?
No strict prerequisites, but familiarity with Python, ML concepts, and AWS basics is recommended. Hands-on experience with cloud services helps. Techpratham advises starting with small projects to understand model behavior before scaling.
How does prompt engineering improve AI outputs?
By crafting clear, context-rich, and structured inputs, models generate more accurate and relevant outputs. Iterative refinement ensures continuous improvement. Techpratham points out that small changes in prompts can significantly affect the results.
How should I prepare for learning Generative AI on AWS?
Preparation includes taking AWS Skill Builder courses, doing hands-on labs in SageMaker and Bedrock, experimenting with prompts, and studying model behavior. Techpratham recommends building end-to-end mini-projects for practical exposure.
What types of applications can I build?
You can create content generation apps, chatbots, code assistants, image generators, summarizers, and business analytics tools. Techpratham highlights that the possibilities expand with creativity and proper prompt design.
What job roles can this skill help with?
Roles include AI Developer, ML Engineer, Data Scientist, Cloud AI Specialist, and AI Product Developer. Techpratham notes that these skills are in high demand for companies adopting generative AI solutions.
Is Generative AI difficult to learn?
It can be challenging initially due to ML concepts and model behavior understanding, but AWS services simplify infrastructure management. Techpratham stresses that consistent practice with real-world prompts and iterative testing is key to mastering it.
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