What you'll learn:
- Understand the fundamentals of Generative AI, including Transformers, Diffusion Models, and their relevance to software engineering.
- Access a curated 1000+ expert prompts tailored to accelerate software engineering tasks across coding, testing, DevOps, architecture, and security.
- Differentiate clearly between Predictive AI and Generative AI in the context of software development workflows.
- Explore real-world use cases of GenAI for code generation, bug fixing, documentation, DevOps automation, and architecture design.
- Master Prompt Engineering techniques: Zero-shot, Few-shot, Chain of Thought (CoT), Tree of Thought (ToT), and reusable prompt templates.
- Generate high-level software architectures, including ER diagrams, sequence diagrams, and make architectural trade-off analyses using GenAI.
- Auto-generate multi-file codebases, classes, modules, and functions while adhering to SOLID and DRY principles.
- Perform code refactoring, enhance readability, optimize performance, and add professional-grade documentation using AI assistance.
- Automate static code analysis, bug detection, anti-pattern recognition, and pull request reviews via Generative AI prompts.
- Learn how to generate Unit Tests, Integration Tests, E2E Tests, API Tests, Fuzz Tests, and achieve better code coverage.
- Build Dockerfiles, Kubernetes manifests, Terraform scripts, and automate GitHub Actions/GitLab CI/CD pipelines using GenAI.
- Design robust Infrastructure as Code (IaC) systems and automate monitoring setups with Prometheus and Grafana using prompt-driven workflows.
- Define and monitor Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to maintain operational excellence.
- Create automated runbooks and disaster recovery playbooks driven by AI to boost reliability engineering practices.
- Implement Secure Code Generation, threat modeling, vulnerability detection, and automate SOC2, HIPAA, GDPR compliance drafts.
- Apply AI-based tools for Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST).
- 1000+ AI Native Software Engineering Prompts
- 20 Practical AI Agent Projects for Software Developers
The "Generative AI for Software Engineers & Developers" course is designed to empower modern developers with the skills to integrate cutting-edge AI tools into the software development lifecycle. Beginning with a solid foundation, the course explains What is Generative AI through real-world examples, followed by an exploration of how GenAI works, covering Transformer and Diffusion models. Learners will clearly differentiate predictive AI from generative AI in software contexts, understanding how GenAI transforms tasks like code generation, bug fixing, documentation, DevOps automation, and architecture design. Practical examples include working with GPT-4, Claude 3, Codex, Gemini 1.5, and CodeLlama.
A deep dive into the architecture of LLMs explains Transformer Networks and Self-Attention, alongside concepts like tokenization, context windows, and model limitations. Learners will compare fine-tuning vs in-context learning and study specialized code LLMs like Codex, StarCoder, CodeGen, and AlphaCode. Hands-on sessions introduce accessing model APIs via OpenAI, Hugging Face, and Anthropic. The course also builds expertise in prompt engineering covering effective principles, zero-shot, one-shot, few-shot prompting, Chain of Thought (CoT) and Tree of Thought (ToT) techniques, and creating reusable prompt templates.
Moving into application design, learners will explore AI-suggested architecture patterns, generate ER diagrams, sequence diagrams, conduct architectural trade-off analyses, and evaluate technology stacks. Practical coding modules teach multi-file code generation, class/module/function creation, code refactoring using SOLID/DRY principles, adding documentation, and GenAI-driven PR reviews. Further sections focus on static analysis, bug detection, unit/integration testing, Dockerfile/Kubernetes manifest generation, IaC scripting, and monitoring setup using Prometheus and Grafana.
Security is integrated through secure code generation, threat modeling prompts, compliance automation (SOC2, HIPAA, GDPR), and AI in SAST/DAST. Finally, learners receive access to a curated 1000+ prompts specifically designed for boosting software engineering productivity with Generative AI.
New Section added:
This section 12- 1000+ AI Native Software Engineering Prompts contains 50 advanced prompt topics designed for software engineers, developers, architects, DevOps engineers, security professionals, and AI product builders who want to move beyond basic AI usage and start building real-world AI-powered engineering systems. These prompts cover the most important areas of modern Generative AI development, including Retrieval-Augmented Generation, vector databases, embeddings, LangChain, LangGraph, AI agents, multi-agent workflows, enterprise search, knowledge graphs, AI-native application architecture, LLM observability, prompt security, AI governance, and autonomous software delivery.
Each topic includes practical, expert-level prompts that can be used to design systems, generate architecture plans, automate development workflows, improve documentation, analyze codebases, strengthen security, manage AI risks, optimize costs, and build enterprise-ready AI copilots. The purpose of this section is to help learners understand how Generative AI can be applied across the complete software development lifecycle, from requirement analysis and coding to testing, deployment, monitoring, security operations, governance, and incident response.
By working through these 50 prompt topics, learners will develop a strong practical understanding of how AI is transforming modern software engineering. This prompt library can be used as a reference guide, practice resource, productivity toolkit, and idea bank for building production-ready AI applications and AI-assisted engineering workflows.
This course is designed for learners who want to build practical skills in GenAI, Generative AI, prompt engineering, and modern Generative AI tools. The course also helps you understand how to write effective prompts, improve AI-generated responses, select the right AI tool for different tasks, and apply Generative AI concepts in real-world situations. Whether you are a beginner, developer, student, professional, entrepreneur, or business leader, this course will help you strengthen your understanding of Generative AI applications, prompt design, AI workflows, large language models.
This course gives you access to 1,000+ practical AI prompts that you can use with your preferred Generative AI tool, including ChatGPT, Google Gemini, and Claude. Instead of being limited to one platform, you can choose the AI assistant that best fits your needs and apply the prompts to workplace, business, productivity, career development, and everyday problem-solving. Each prompt can be copied, customized, and adapted across different AI platforms, helping you improve your prompt engineering skills and achieve more accurate, relevant, and useful results.
New Section 13 Added - 20 Practical AI Agent Projects for Software Developers
This newly added section provides 20 practical AI agent projects designed specifically for software developers who want to move beyond AI theory and understand how modern agentic applications are actually designed and implemented.
Throughout this section, learners will explore AI agents through step-by-step, code-focused projects explained in simple, beginner-friendly language. Each project introduces a different agent architecture, development pattern, or real-world use case, allowing learners to understand not only how to build an agent but also why the architecture works and when it should be used.
The projects cover a wide range of modern AI agent development concepts, including automated code generation and debugging, Retrieval-Augmented Generation (RAG), vector database retrieval with Qdrant, LangGraph workflows, multi-agent planning, documentation automation, natural-language-to-SQL systems, data visualization agents, web-search agents, supervisor architectures, customer-support workflows, and collaborative multi-agent systems.
Learners will also progress into more advanced agent engineering concepts such as RAG-powered group conversations, LLM application evaluation with AgentEval, agent observability and runtime monitoring with AgentOps-style techniques, and automatically generated multi-agent architectures using an AgentBuilder approach.
Rather than treating every AI agent as a simple chatbot, these projects demonstrate how agents can reason about tasks, retrieve information, use tools, generate and validate outputs, collaborate with other agents, delegate responsibilities, monitor execution, evaluate AI responses, and dynamically create specialized agent teams.
By completing this section, learners will gain practical exposure to several important patterns used in modern agentic software development and develop a stronger understanding of how individual components can be combined into larger AI-powered applications.
The section includes these 20 hands-on AI agent projects:
Automated Code Generation, Execution & Debugging Agent
Retrieval-Augmented Code Generation & Q&A Agent
Qdrant-Based Code Generation & Q&A Agent
LangGraph Code Assistant
Coding & Planning Multi-Agent System
README Generator Agent
Markdown Validator Agent
Landing Page Generator Agent
Natural Language-to-SQL Agent
LangGraph SQL Agent
Data Visualization Multi-Agent System
Web Search Tool Agent
Three-Agent Group Chat System
Supervisor Multi-Agent Workflow
Information Gathering Agent
Customer Support Graph Agent
RAG Group Chat Agent
AgentEval Multi-Agent Assessment System
AgentOps Observability Agent
AgentBuilder Auto-Generated Multi-Agent System
By the end of this section, learners will have moved from building individual AI-powered developer utilities to understanding RAG agents, tool-using agents, stateful workflows, multi-agent collaboration, supervision, evaluation, observability, and dynamically generated agent systems—providing a practical foundation for building more sophisticated agentic AI applications for software development.
