Generative Ai For Software Engineers & Developers
Published 4/2025
Created by Edcorner Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 96 Lectures ( 3h 7m ) | Size: 903 MB
1000+ GenAI Prompts to Accelerate Your Software Engineering Journey
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).
Requirements
Basic understanding of software development concepts
No prior experience with AI or Machine Learning is required
A curious mindset ready to explore Prompt Engineering, Generative AI APIs, and new coding techniques powered by AI tools.
Access to a laptop or computer with Internet connectivity
Description
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.
Who this course is for
Software Engineers aiming to leverage Generative AI tools for faster and smarter coding, architecture, and DevOps workflows.
Backend Developers who want to auto-generate APIs, improve system designs, automate deployments, and optimize application performance using AI.
Frontend Developers interested in using AI to boost UI/UX workflows, auto-generate React/Vue components, and optimize frontend performance.
DevOps Engineers looking to automate infrastructure creation, monitoring setups, and CI/CD pipelines with Generative AI prompts.
Solution Architects wanting to use AI for generating architecture diagrams, conducting trade-off analysis, and suggesting technology stacks.
Quality Assurance (QA) Engineers and Test Automation Engineers who wish to auto-generate unit tests, API tests, E2E tests, and fuzz tests using GenAI.
Security Engineers aiming to integrate secure code generation, vulnerability scanning, and compliance automation into the software lifecycle.
Technical Leads and Engineering Managers seeking to empower their teams with AI-driven development accelerators and best practices.
Students, Learners, and Fresh Graduates in Computer Science or related fields aspiring to learn the future of AI-driven software engineering.
Anyone curious about applying Generative AI in practical, real-world coding, testing, architecture, and DevOps workflows.
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