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Generative AI for Software Developers

Transform your coding workflow and career by mastering Generative AI techniques, the definitive skill set for the modern software developer

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About This Course

Are you ready to move beyond basic scripts and truly leverage Generative AI to revolutionize your development process? The role of the software developer is fundamentally changing, demanding specialized knowledge in how to strategically integrate AI into production systems. This highly specialized course moves you past simple helper functions and dives deep into architecting, securing, and operationalizing intelligence across the entire SDLC.

You will master the foundational models (LLMs, SLMs, and LMMs), learn professional Prompt Engineering techniques for maximum efficiency, and explore GenAI Ops to deploy robust AI-powered applications. Whether you are aiming for faster Code Generation, designing complex multi-AI Agents, or leading a team in Model Fine-Tuning, this program provides the practical, hands-on knowledge to design and launch advanced Generative AI solutions from prototype to production.

Skills You’ll Get

  • Foundations & Code Generation: Master the art and science of Generative AI, distinguishing between various foundation models (LLMs), and leveraging core concepts and tools for efficient and accurate Code Generation to accelerate your daily tasks.
  • Architecture & Prompt Engineering: Dive into Generative AI application architectures and design, applying advanced Prompt Engineering techniques and prompt management cycles to optimize model performance across the Software Development Life Cycle (SDLC).
  • Integration & Production Readiness: Learn to integrate AI seamlessly into the SDLC, mastering GenAI Ops for operationalizing and scaling Generative AI applications, including crucial Model Fine-Tuning strategies for building well-architected systems.
  • Agentic AI, Security, & Ethical AI: Explore the future with Reinforcement Learning and the creation of sophisticated multi-AI Agents, while establishing essential security architecture, guardrails, and Ethical AI practices to mitigate bias and privacy concerns.
Download Course Outline

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 Pillars 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

  • Getting Started with Generative AI
3

Generative AI Architecture Fundamentals

  • Applying Advanced Techniques for Controlling ChatGPT
  • Understanding Generative AI Architecture Fundamentals
4

Generative AI in Software Development

  • Understanding 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)
  • Integrating Generative AI into the SDLC
7

Ethical and Security Best Practices in Generative AI

  • Shaping the Future of Prompt Engineering
8

Generative AI Application Architecture and Design

  • Designing and Architecting Generative AI Applications
9

Well-Architecting and Fine-tuning GenAI Application

  • Generating and Learning with Agentic AI
10

Building a GenAI App from Prototype to Production

  • Tuning and Benchmarking GenAI

Any questions?
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This program is ideal for software developers, application architects, MLOps engineers, and anyone responsible for integrating or deploying Generative AI features into commercial software applications.

It covers the full Prompt Engineering spectrum, from basic prompting techniques to advanced concepts like ReAct, Self-Consistency, and RAG, ensuring you can optimize LLMs for high-quality, reliable outputs in the SDLC.

Yes, the course includes dedicated content on Reinforcement Learning (RL) and AI Agents, focusing on how to design and implement sophisticated multi-agent systems and leveraging function calling with large language models.

The course is heavily focused on practice, covering application architecture, Model Fine-Tuning, and providing hands-on labs that guide you through building a production-ready Generative AI application from prototype to post-production monitoring.

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