Generative AI for Software Engineers & Developers

• 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).

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.