Module 1: Introduction to Generative AI

• What is Generative AI?
• Evolution from ML to LLMs
• Generative AI vs traditional ML
• Use cases across industries
• GenAI ecosystem overview

Module 2: Foundations of Large Language Models (LLMs)

• What are LLMs?
• Tokenization & embeddings
• Transformer architecture (conceptual)
• Training vs inference
• Limitations of LLMs

Module 3: Prompt Engineering

• Prompt engineering fundamentals
• Zero-shot, few-shot prompting
• Prompt templates & chaining
• Prompt optimization techniques
• Safety & hallucination handling

Module 4: AWS Cloud for Generative AI

• AWS global infrastructure for AI
• IAM & security for AI workloads
• Cost models for GenAI services
• Selecting the right AWS GenAI services

Module 5: Amazon Bedrock Deep Dive

• Amazon Bedrock architecture
• Foundation models overview
• Model selection strategies
• API-based GenAI development
• Customization & guardrails

Module 6: Building GenAI Applications on AWS

• Text generation applications
• Chatbot development
• Summarization & Q&A systems
• Image & multimodal use cases
• Integrating GenAI with web apps

Module 7: Embeddings & Vector Databases

• What are embeddings?
• Semantic search concepts
• Vector similarity search
• AWS-based vector storage options
• Use cases in GenAI apps

Module 8: Retrieval Augmented Generation (RAG)

• RAG architecture & workflow
• Data ingestion & indexing
• Context injection techniques
• Improving accuracy with RAG
• Enterprise GenAI use cases

Module 9: Model Customization & Fine-Tuning (Conceptual)

• Fine-tuning vs prompt tuning
• Parameter-efficient tuning (PEFT)
• When to customize models
• Cost & performance trade-offs

Module 10: GenAI Application Deployment

• Serverless GenAI architectures
• API-based deployment
• Scaling & latency optimization
• Monitoring & logging
• Cost optimization strategies

Module 11: Responsible AI, Security & Compliance

• Ethical AI principles
• Bias & fairness
• Data privacy & compliance
• Guardrails & content moderation
• Security best practices in GenAI

Module 12: Integrating GenAI with Enterprise Systems

• GenAI + databases
• GenAI + analytics platforms
• Automation & workflow integration
• Real-world enterprise scenarios

Module 13: Exam Preparation – AWS Generative AI Developer (Professional)

• Exam domains & structure
• AWS exam blueprint
• Sample questions & mock tests
• Exam tips & strategies
• Final revision sessions

Module 14: Capstone Projects

• GenAI chatbot using Amazon Bedrock
• RAG-based enterprise Q&A system
• AI content generation application
• End-to-end GenAI deployment project

Module 15: Career & Placement Support

• GenAI career roadmap
• Resume & portfolio building
• GitHub project guidance
• Interview preparation (GenAI + AWS)
• Mock interviews & placement assistance