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Using GenAI in Modern Software Development
Course
Using GenAI in Modern Software Development
Master the use of generative AI to analyze code, streamline workflows, and accelerate development. Gain hands-on experience with tools like GitHub Copilot and ChatGPT to improve coding practices and documentation.
Flexible Schedule
Intermediate Level
Mentor Support
Estimated Time
5 weeks
3-4 hours/week
3-4 hours/week
In todays fast-evolving software landscape, success increasingly relies on the ability to integrate AI tools strategically throughout the development lifecycle. This course teaches developers how to use generative AI to analyze, enhance, and streamline software workflows.
Building on foundational coding and AI concepts, youll learn to use generative AI as both a decision-making aid and a practical coding assistant. The course emphasizes real-world applications, guiding you to analyze unfamiliar code, evaluate frameworks, and create documentation with the support of AI tools. Through hands-on experience with platforms like GitHub Copilot and ChatGPT, youll gain the skills to integrate AI tools into everyday development tasks.
This course is designed for developers who want to improve coding practices using generative AI tools. By the end of the course, youll be equipped to apply AI-powered solutions that elevate your coding practices and development processes.
Enroll today to gain valuable, future-ready development insights.
This course comprises five purposely designed modules that take you on a carefully defined learning path.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 3-4 hours per week, you will complete the course in 5 weeks. However, as long as the course is completed before the end date, you can work at your own pace.
The materials for each module will become available when you start the particular module. Methods of learning and assessment will include videos, reading material, online exams questions, and a final peer review assignment.
Once you have successfully completed the course, you will earn your Certificate of Completion.
By the end of this course, you will be able to:
- Develop applications efficiently and effectively.
- Perform thorough code reviews to ensure quality and maintainability.
- Debug code and resolve issues systematically.
- Leverage generative AI for various development tasks.
- Design and optimize prompts for AI systems.
- Software developers/engineers with 2+ years experience
- Technical leads or managers looking to integrate AI into workflows.
- At least 2 years of software development experience
Course Outline
Video: Welcome to Software Development Using AI (3:50)
Reading: General Information
Reading: Grading Scheme
Reading: How to Succeed in This Course
Reading: Helpful Tools and Resources
Module Introduction and Learning Objectives
Video: What is Generative AI? (2:58)
Reading: History and Evolution of AI in Software Development
Reading: Introduction to Key AI Concepts for Developers
Video: Popular AI Tools in Software Development (5:17)
Reading: Introduction to AI Development Tools
Video: Exploring AI Tools in Software Development (3:59)
Video: Basics of Prompt Engineering (5:03)
Video: Crafting Effective Prompts (3:15)
Reading: Introduction to Prompt Engineering for Software Development
Lab: AI Prompting Practice
Video: Role of AI in the SDLC (5:23)
Video: Applying AI Tools Across Software Development Phases (5:01)
Reading: Key Considerations for Integrating AI into Development Processes
Reading: Module Summary: Introduction to Software Development Using AI
Practice Quiz: Introduction to Software Development Using AI
Graded Quiz: Introduction to Software Development Using AI
Module Introduction and Learning Objectives
Video: AI in Code Analysis (3:56)
Video: Navigating Codebases with AI (5:32)
Reading: Preparing Code for AI Analysis
Reading: Exploring Large Codebases with AI
Reading: How AI Identifies Key Components, Entry Points, and Dependencies
Video: AI and Codebase Visualization (3:54)
Lab: Use AI to Summarize an Open-Source Codebase
Video: Understanding AI in Code Analysis (3:55)
Reading: Understanding Functions, Variables, and Interactions with AI
Video: Using AI to Trace Variables and Fix Bugs (4:38)
Lab: AI Analysis of a Code Function
Reading: Module Summary: Code Understanding and Analysis
Practice Quiz: Code Understanding and Analysis
Graded Quiz: Code Understanding and Analysis
Module Introduction and Learning Objectives
Video: Introduction to AI-Powered Code Generation (6:50)
Reading: Common Use Cases for AI Code Generation
Video: Capabilities of AI in Code Generation (6:56)
Video: Crafting Effective Prompts for Code Generation (7:10)
Reading: Examples of AI Prompts for Coding Tasks
Video: How Prompts Influence AI-Generated Code (7:30)
Reading: Best Practices for Writing AI Prompts
Lab: Write AI Prompts to Generate Code Snippets
Reading: Using AI for Testing and Debugging
Video: Step-by-Step Guide to Debugging Code with AI (5:10)
Reading: AI in Testing and Debugging – Methods and Best Practices
Reading: Module Summary: Code Generation Fundamentals
Practice Quiz: Code Generation Fundamentals
Graded Quiz: Code Generation Fundamentals
Module Introduction and Learning Objectives
Video: Introduction to AI-Generated Documentation (6:52)
Reading: Key Elements of Technical Documentation
Reading: Best Practices for AI-Assisted Documentation
Lab: Generate a Sample Document Using AI
Reading: Importance of Standardization in Software Documentation
Reading: Frameworks for Consistent Documentation
Video: AI Tools for Enforcing Documentation Standards (7:10)
Lab: Use AI to Standardize an Existing Documentation Template
Video: AI for Team Communication & Knowledge Sharing (7:54)
Reading: AI Tools for Enhancing Developer Communication
Video: Using AI Tools to Enhance Team Communication (7:29)
Reading: Module Summary: Documentation and Communication for Software Development
Practice Quiz: Documentation and Communication for Software Development
Graded Quiz: Documentation and Communication for Software Development
Module Introduction and Learning Objectives
Graded Quiz: Using GenAI in Modern Software Development
Final Project: AI-Powered Code Review and Documentation for a Software Project
Final Project: Submission and Evaluation
Video: Course Summary (4:52)
Glossary: Using GenAI in Modern Software Development
Reading: Congratulations and Next Steps
Reading: Thanks from the Course Team
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
This course introduces developers to GenAI in software development, focusing on how generative AI tools like GitHub Copilot and ChatGPT can enhance every stage of the development lifecycle. It covers practical methods for debugging, documentation, and intelligent code automation.
The AI code generation course helps learners master software development using AI by showing how to apply AI tools for rapid coding, refactoring, and documentation. Youll learn to analyze code patterns, optimize workflows, and increase productivity using AI assistance.
This AI coding course online builds skills in AI code assistance, debugging with generative AI, prompt engineering for developers, and AI-powered code review. Youll also gain hands-on experience using AI tools to automate repetitive coding tasks and generate accurate technical documentation.
This software development using AI course is ideal for developers, engineers, and technical professionals who want to integrate AI in software development workflows. Its also suitable for learners exploring AI-powered automation in coding, testing, and documentation.
Yes, a basic understanding of programming concepts is helpful. However, this AI code generation course introduces step-by-step use of AI tools, making it accessible even to early-career developers who want to enhance their coding capabilities with AI.
Yes. Learners apply GenAI in software development through hands-on labs using Copilot, ChatGPT, and other AI tools to generate, debug, and optimize code while practicing intelligent workflow design.
Youll gain experience with AI in software development platforms like GitHub Copilot and ChatGPT. The course also explores techniques for selecting libraries, frameworks, and integrations with AI-powered development environments.
By leveraging AI-powered code review and automated documentation, this course demonstrates how AI in software development ensures cleaner code, faster debugging, and improved maintainability through continuous assistance and intelligent recommendations.
Completing this AI agent developer course equips you with in-demand skills for AI-assisted coding roles, automation engineering, and DevOps integration. It prepares you for future opportunities where GenAI in software development is essential for innovation.
Yes. Learners receive a shareable Certificate of Completion, validating their ability to apply AI in software development for building efficient, scalable applications.
Type of certificate
IBM Certificate
About this course
05 Modules
05 Skills
Includes
Discussion space
06 Hands-on labs
04 Practice quizzes
04 Graded quizzes
01 Final project
01 Final exam
Create
Prompts to Generate Code Snippets
Sample Document Using AI
Exercises to explore
AI Prompting Practice
Summarize an Open-Source Codebase
Analysis of a Code Function
Standardize an Existing Documentation Template
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