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Generative AI for Software Developers

(GENAI-SOFTDEV.AJ1)
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1

Preface

  • Who is this course for
  • Why This course Matters Now
  • What this course covers
  • Accessing Code
  • To get the most out of this book
  • Errata
2

The Art and Science of Generative AI

  • What is Generative AI?
  • Generative AI Use Cases
  • Generative AI Benefits
  • Myths Around Generative AI
  • Challenges of Generative AI
  • Generative AI for Software Development
  • How Developers Should Evolve with Generative AI
  • Summary
3

Getting Started with Generative AI

  • Expanding Your Generative AI Knowledge: SLMs, LLMs, and LMMs
  • Foundation Models in Generative AI
  • How to Start with Generative AI
  • Code Generation Using Generative AI
  • Agentic AI Workflows
  • How Generative AI is Becoming Democratized
  • Summary
4

Generative AI Architecture Fundamentals

  • Understanding Generative AI Models Architecture
  • Category of Generative AI Models and Their Architecture 
  • Approaches of Generative Models
  • Hyperparameter Tuning and Regularization
  • Model Evaluation Techniques
  • Choosing the Right Generative Model for Specific Use Cases
  • Best Practices for Model Evaluation
  • Summary
5

Generative AI in Software Development

  • Impact of Generative AI on Software Development
  • Essential Tools and Frameworks for Gen AI-Based Software Application Development
  • GenAI Ops: Operationalizing Generative AI Applications
  • Summary
6

Prompt Engineering For Software Developers

  • Why Prompt Engineering?
  • Prompt Techniques
  • Prompt Use Cases for the Software Development Lifecycle (SDLC)
  • Prompt Management Cycle and Best Practices 
  • Prompt Engineering Tools
  • Summary
7

Integrating Generative AI into the Software Development Cycle

  • Industry Study on Developer Productivity with Generative AI
  • Transforming Software Development with Generative AI in the SDLC
  • Generative AI for Specific Programming Tasks
  • End-to-End AI Integration in the SDLC
  • Challenges and Tradeoffs in AI Integration
  • Key Metrics and KPIs for Measuring AI Impact 
  • Next Steps: Sustaining and Expanding AI Integration
  • The Future Outlook 
  • Summary
8

Ethical and Security Best Practices in Generative AI

  • Why the New Concerns?
  • Bias in AI-Generated Code
  • Model Architecture and Optimization Bias
  • Human Feedback Bias
  • Strategies to Mitigate Bias 
  • Prompt Safety and Security for Responsible AI
  • Intellectual Property (IP) Considerations
  • Privacy Concerns in Generative AI
  • Key AI Laws and Guidelines
  • Security Risks in AI Applications
  • Security Architecture for Generative AI Apps
  • Guardrails for Secure Use of Generative AI Applications
  • Observability from an Ethical AI Perspective
  • Summary
9

Generative AI Application Architecture and Design

  • Principles of Generative AI Application Architecture
  • Text Generation Architecture
  • Text Summarization Architecture
  • Q&A (Question and Answer) App architecture 
  • Chatbot Architecture
  • Image and Video Generation App Architecture 
  • GenAI Architecture for Industry Use Cases
  • Summary
10

Reinforcement Learning and AI Agent Architecture Design

  • What is Reinforcement Learning?
  • Reinforcement Learning with Human Feedback 
  • Automated Reinforcement Learning (AutoRL) 
  • GenAI Agents
  • Agentic AI 
  • Building an Intelligent Travel Assistant
  • GenAI Multi-Agent Systems
  • Function Calling with LLMs
  • Summary
11

Well-Architecting and Fine-tuning GenAI Application

  • What is Model Fine-Tuning?
  • Model evaluation
  • LLM Benchmarking
  • Building Well-Architected Gen AI Applications
  • Well-Architected Framework Pillers for GenAI Applications
  • Summary
12

Building a GenAI App from Prototype to Production

  • Building SkillGenie - Problem Statement 
  • SkillGenie – Features
  • SkillGenie User Journey 
  • System Design for SkillGenie
  • API Design 
  • Prototype Development
  • Safe use of AI and content moderation
  • Enhancing SkillGenie outputs using Agentic AI 
  • Production Launch
  • Post-Production Monitoring 
  • Summary

1

The Art and Science of Generative AI

  • Mastering Generative AI
2

Getting Started with Generative AI

3

Generative AI Architecture Fundamentals

  • Applying Advanced Techniques for Controlling ChatGPT
4

Generative AI in Software Development

5

Prompt Engineering For Software Developers

  • Exploring Different Prompt Styles
  • Exploring Advanced Prompting Techniques – Self-Consistency, ReAct, and RAG
  • Applying Basic AI Prompting to SDLC Activities
6

Integrating Generative AI into the Software Development Cycle

  • Exploring AI-Prompt Use Cases Across the SDLC (Software Development Life Cycle)
7

Ethical and Security Best Practices in Generative AI

  • Shaping the Future of Prompt Engineering
8

Generative AI Application Architecture and Design

9

Reinforcement Learning and AI Agent Architecture Design

10

Well-Architecting and Fine-tuning GenAI Application

11

Building a GenAI App from Prototype to Production

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