Gen AI for Software Development – for Students

Instructor: Arun Kumar A R Instructor - Brisa Technologies

Centre for Development of Advanced Computing (C-DAC)

Course Fees: False

Enroll Now

Starting On:
July 10, 2025

Format:

Offline

Language:

English

Duration:

30 hours + 12 hours of Mini-project

Certificate:

Digital Copy

Audience:

Students looking to build skills in AI-driven software...

Course Overview

This 30-hour course, followed by a 12-hour mini-project, introduces engineering students to the fundamentals of the Software Development Life Cycle (SDLC), modern Agile practices, and the transformative role of Generative AI (GenAI) across the software development spectrum. Students will learn how tools like Large Language Models (LLMs) can enhance productivity and innovation in development, testing, DevOps, and production support. The course blends foundational concepts, practical demos, and hands-on activities to prepare students for the AI-powered future of software engineering.

Notes: · Each session will start with a recap of the previous session and Q&A for the previous session. · Each session will end with the trainer describing homework / assignment/

What You'll Learn

Basics of Artificial Intelligence, Machine Learning, and Generative AI

How LLMs work and how to interact with them using Prompt Engineering

Traditional vs. Agile SDLC methodologies

AI-assisted code generation, bug fixing, refactoring, and documentation

Automating test case generation, test data, and regression testing using GenAI

Leveraging AI for CI/CD pipelines and Infrastructure as Code (IaC) in DevOps

Using GenAI in production monitoring, support, and automated documentation

Applying GenAI skills in a mini-project simulating a real-world SDLC workflow

Why Enroll in this Course?

Future-Proof Your Career: Generative AI is transforming the way software is designed, built, tested, and maintained. This course equips you with in-demand skills that top tech companies are actively seeking.

Learn by Doing: With hands-on activities, assignments, and a guided mini-project, you’ll not only understand how GenAI works—you’ll apply it in real-world software development scenarios.

Bridge the Industry-Academia Gap: Get ahead of the curve by learning AI-powered software engineering practices that go beyond classroom theory and align with current industry trends.

Master Tools You’ll Actually Use: From AI-assisted code generation to test automation and DevOps orchestration, you’ll learn how to integrate GenAI tools like ChatGPT, GitHub Copilot, and others into your daily workflow.

Boost Your Internship & Job Readiness: Stand out in placement interviews and project evaluations with your ability to demonstrate practical GenAI applications across the software lifecycle.

No AI Background Required: Designed for students with basic programming knowledge, this course provides a gentle yet powerful introduction to Generative AI and its practical uses in software development.

Course Syllabus

Generative AI Fundamentals

Introduction to AI and ML: High-level overview of Artificial Intelligence and Machine Learning concepts.

What is Generative AI? Definition, core capabilities, and its emerging role in software engineering.

How LLMs Work: High-level understanding of Large Language Models, their architecture, and text generation mechanism.

Prompt Engineering Basics: Principles and practical techniques for effective interaction with LLMs to generate desired outputs.

Introduction to Software Development Life Cycle (SDLC)

What is SDLC? Definition, importance, and benefits of a structured approach to software development.

SDLC Phases: Detailed exploration of key stages: Planning, Feasibility Analysis, System Design, Implementation, Testing, Deployment, and Maintenance.

Traditional SDLC Models: Brief overview of models like Waterfall to understand their characteristics.

Project Management & Quality Assurance in SDLC: Understanding their significance within the SDLC framework.

Modern SDLC Methodologies: Agile & Scrum

Limitations of Traditional SDLC: Discussing challenges with rigid, sequential models.

Introduction to Agile: Core principles, values, and the Agile Manifesto.

Scrum Framework: Roles: Product Owner, Scrum Master, Development Team.

Scrum Framework: Artifacts: Product Backlog, Sprint Backlog, Increment.

Scrum Framework: Events: Sprint, Sprint Planning, Daily Standup, Sprint Review, Sprint Retrospective.

Generative AI in the Development Phase

AI-assisted Code Generation: Generating code snippets or entire applications from high-level specifications, ensuring consistency and adherence to standards.

Pair-programming with LLMs: Leveraging LLMs for real-time code suggestions, completion, and optimizing code quality.

Code Analysis & Improvement: Using LLMs to identify bugs, analyze code for efficiency, security vulnerabilities, and assist in refactoring.

Code Translation & Documentation: Assistance in translating code between languages and generating technical documentation.

Assignment / Hands-on activity

Generative AI in the Testing Phase

Automated Test Case Generation: Using GenAI to create comprehensive test cases and functional tests.

Test Data Generation: Automating the creation of realistic test data.

Bug Detection & Anomaly Identification: Leveraging AI for early detection of issues and anomalies.

Regression Testing Automation: Streamlining repetitive testing processes.

Performance & Security Testing with LLMs: Designing performance tests to identify bottlenecks and analyzing code for potential security risks.

Assignment / Hands-on activity

Generative AI in DevOps

AI-assisted Monitoring: Utilizing Generative AI for performance monitoring and identifying issues in production environments.

Remedy Suggestion: AI's role in suggesting solutions for production incidents and problems.

AI-assisted Support: Implementing AI tools for troubleshooting and providing support.

Automated Documentation for Maintenance: Generating and updating necessary documentation for ongoing software maintenance.

Assignment / Hands-on activity

Integrated Project & Review

Real-World Case Studies: Discussing examples of Generative AI's impact across the SDLC.

Key Concepts Review: Comprehensive review of all topics covered and Q&A session.

Mini-Project/Exercise: Practical application of GenAI tools in a simulated SDLC scenario (e.g., generating a simple code module, its tests, and documentation using an LLM).

Instructor Profile

Arun Kumar A R

AI & ML | Data Science & Analytics | AI-ML Model Building | Generative AI | Corporate Trainer

Testimonials

Real stories from developers who upskilled, built projects, and advanced their careers with
GenAInstein Student Internship Program.

The course helped me truly understand how to work with LLMs and build GenAI apps from scratch. The hands-on projects and mentorship gave me the confidence to apply these skills at work.

Rahul M.

Software Engineer

As a student, I was overwhelmed by all the AI hype. GenAInstein's course broke it down clearly and helped me build actual working apps. I even added one to my portfolio!

Sneha K.

Final Year Student

What stood out was how practical everything was. I wasn't just learning theory—I was building things, using RAG pipelines, and even integrating APIs. This course is gold for developers.

Arjun S.

Developer

The curriculum was very well-structured, and the mentors were incredibly helpful. It's rare to find a course that balances cutting-edge content with hands-on coding so well.

Priya D.

Tech Lead

Certification Details

Free Access: NO [Download Brochure for further details]

Certification Exam: [Download Brochure]

Certification Criteria: Minimum 75% score required

Certificate Type: Digital e-certificate (no hard copies)

Exam Date: To be announced after course completion