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Generative AI in Software Development

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Generative AI in Software Development

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply generative AI to code generation, debugging, and testing for faster, error-free development workflows.

  • Build expertise in AI-powered developer tools like GitHub Copilot, ChatGPT, and CodeWhisperer for software engineering.

  • Implement embeddings, retrieval-augmented generation (RAG), and fine-tuning to optimize AI-driven applications.

  • Evaluate ethical issues, collaboration models, and future trends in AI-powered software engineering.

Details to know

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Assessments

7 assignments

Taught in English

There are 2 modules in this course

The course provides a comprehensive exploration of how generative AI is reshaping software development by accelerating coding, improving debugging, and enhancing automation. It is designed for aspiring software engineers, developers, and professionals who want to integrate AI into modern development workflows to build efficient, scalable, and error-free applications.

You will explore the role of large language models (LLMs) like GPT, Gemini, and LLaMA in coding tasks, software testing, and project automation. The course begins with foundational AI concepts—machine learning, deep learning, and generative models—before diving into practical applications of code generation, prompt engineering, and debugging. Hands-on labs and exercises guide you through AI-powered developer tools such as GitHub Copilot, ChatGPT, and CodeWhisperer. You will also examine advanced AI topics including embeddings, retrieval-augmented generation (RAG), and fine-tuning to customize AI models for specific development needs. Ethical considerations, human-AI collaboration, and the future of AI in engineering are also covered. By the end of this course, you will be able to: - Apply generative AI to accelerate coding, debugging, and software testing. - Use AI-powered tools like Copilot, ChatGPT, and CodeWhisperer to improve productivity. - Implement advanced AI methods such as embeddings, RAG, and fine-tuning. - Evaluate ethical, collaborative, and practical challenges of AI in software engineering. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

This module introduces learners to the fundamental concepts of Generative AI and its applications in software development. It covers key AI technologies, including Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning, explaining their differences and real-world use cases. Learners will explore the evolution of Generative AI, comparing it with Discriminative AI, and understand how these models contribute to tasks such as content creation, classification, and predictive analysis. Additionally, the module examines the latest AI models, such as GPT, Gemini, and Copilot, showcasing their role in software engineering. By the end of this module, learners will have a strong foundation in Generative AI, preparing them for advanced applications in coding, automation, and AI-driven software development.

What's included

10 videos2 readings3 assignments1 discussion prompt1 plugin

10 videos•Total 49 minutes
  • Introduction to the Course•2 minutes
  • Meet your Instructor•1 minute
  • Difference between AI, ML, and Deep Learning•6 minutes
  • Comparing Generative AI with Discriminative AI•7 minutes
  • Evolution and Democratization of AI: From GPT to ChatGPT 4.0•6 minutes
  • Key AI Models in Software Development (GPT, Gemini, Copilot, LLaMA)•4 minutes
  • OpenAI Models Overview (GPT, Whisper, DALL-E)•3 minutes
  • Exploring APIs: OpenAI, Gemini, Mistral and Practical Use•6 minutes
  • Practical Demo - Using Mistral with Python/API-Postman•8 minutes
  • Prompt Engineering: Effective Techniques for Developers•7 minutes
2 readings•Total 20 minutes
  • Syllabus•5 minutes
  • Read more about AI: Foundations, Innovations, and Emerging Trends•15 minutes
3 assignments•Total 45 minutes
  • Practice Quiz: Introduction to Generative AI for Software Engineers•15 minutes
  • Practice Quiz: Core Generative AI Models•15 minutes
  • Practice Quiz: Exploring API’s and practical Demo’s•15 minutes
1 discussion prompt•Total 10 minutes
  • Meet and Greet•10 minutes
1 plugin•Total 5 minutes
  • Quick Course Check-In•5 minutes

This module explores the transformative role of AI in modern software engineering. It covers AI-powered code generation, debugging, and optimization, demonstrating how tools like GitHub Copilot, ChatGPT, and CodeWhisperer assist developers in writing efficient, maintainable, and error-free code. Learners will also delve into advanced AI concepts such as embeddings, retrieval-augmented generation (RAG), and fine-tuning, gaining insights into their applications in real-world software development. The module concludes with discussions on the future of AI in software engineering, human-AI collaboration, and the ethical considerations developers must address when integrating AI into their workflows.

What's included

9 videos1 reading4 assignments

9 videos•Total 56 minutes
  • Introduction to AI for Code Generation (GitHub Copilot, ChatGPT, CodeWhisperer)•8 minutes
  • Writing Efficient Code with AI Assistance•7 minutes
  • AI-Driven Debugging: Detecting and Fixing Errors•7 minutes
  • Advanced AI Concepts: Embeddings, RAG, Fine-Tuning•15 minutes
  • Real-World Project Implementation Using AI Models•7 minutes
  • AI and the Future of Software Engineering: Automation vs. Job Creation•4 minutes
  • The Human-AI Collaboration: Redefining the Role of Developers•4 minutes
  • Ethical Considerations in AI Development•4 minutes
  • Course Closure - Gratitude !•1 minute
1 reading•Total 15 minutes
  • Read more about AI in Software Development: Code Generation, Debugging, Ethics, and Future Trends•15 minutes
4 assignments•Total 65 minutes
  • Graded Quiz•20 minutes
  • Practice Quiz: AI-Powered Code Generation and Debugging•15 minutes
  • Practice Quiz: Glimpse into advance Gen AI concepts•15 minutes
  • Practice Quiz: The Future of Generative AI in Software Engineering•15 minutes

Instructor

Board Infinity
261 Courses•428,186 learners

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Frequently asked questions

Generative AI in software development automates coding, debugging, and documentation tasks. Tools like GitHub Copilot and ChatGPT enhance developer productivity, reduce errors, and accelerate software delivery.

You will learn how to apply generative AI to software engineering tasks, including code generation, debugging, testing, and optimization. The course also covers advanced topics like embeddings, retrieval-augmented generation (RAG), and fine-tuning, giving you the ability to build AI-driven applications.

The course provides hands-on practice with popular developer tools such as GitHub Copilot, ChatGPT, and CodeWhisperer. You will use these tools to generate code, fix bugs, and accelerate project workflows.

Developers can use generative AI to auto-generate code snippets, complete functions, and suggest bug fixes. AI models like GPT and Gemini support multiple programming languages, making coding faster and more efficient.

No prior AI knowledge is required, but basic programming skills are helpful. The course is beginner-friendly and guides you step-by-step in applying generative AI techniques in real-world coding environments.

The course covers embeddings, retrieval-augmented generation (RAG), and fine-tuning to customize generative AI models. These concepts prepare you to build domain-specific applications and optimize performance.

Hands-on labs include generating code snippets, debugging with AI, applying RAG for context-aware outputs, and customizing AI-driven applications. These exercises simulate real-world developer tasks.

By completing this course, you’ll gain in-demand skills in AI-powered coding and debugging, making you competitive for roles in software development, DevOps, and AI-driven engineering.

Yes. The course concludes with discussions on the evolving role of AI in development, human-AI collaboration, and the future of automated software engineering.

The course takes about 3 hours to complete, with flexible scheduling so you can learn at your own pace. It balances theoretical concepts with hands-on practice to maximize learning efficiency.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,