Introduction to SDLC and GenAI in SDLC

Instructor: Shyam Prakash Co-founder & CTO - Whatifics

Course Fees: ₹5,999/-

Starting On:
Nov. 29, 2025

Format:

Online Instructor Led Training

Language:

English

Duration:

16 Hours

Certificate:

Digital Copy

Audience:

Software Developers

Course Overview

Generative AI is transforming the way software is designed, developed, and deployed. This course equips software developers with the skills and tools needed to significantly boost productivity using AI across the development lifecycle. Through a mix of theory, demonstrations, and hands-on labs, participants will learn how to integrate AI into code generation, testing, debugging, documentation, database design, and more.Learners will explore tools like GitHub Copilot, ChatGPT, Windsurf, Cursor AI, and Azure Copilot, and apply them to real-world scenarios. The course culminates in a capstone project where participants design and implement a full-stack software feature using AI—from prompt to production.Whether you're building from scratch or working with legacy systems, this course will help you unlock efficiency, improve code quality, and stay ahead in a rapidly evolving tech landscape.

What You'll Learn

This hands-on course is designed to help software developers harness the power of Generative AI to boost productivity across the software development lifecycle. You will:Understand the fundamentals and impact of Generative AI in development.Master prompt engineering to interact effectively with AI tools.Explore and use AI-powered tools like GitHub Copilot, ChatGPT, Cursor AI, Windsurf, and Azure Copilot.Generate and integrate code, unit tests, documentation, and database schemas using AI.Apply AI for debugging, refactoring, and reverse engineering legacy codebases.Build full-stack features using AI, from UI to backend, with hands-on projects.Learn the ethical implications, limitations, and best practices in AI-assisted development.

Why Enroll in this Course?

Future-Proof Your Skills: Stay ahead with cutting-edge AI capabilities transforming the developer landscape.

Highly Practical: Includes IDE setup, real-time prompt labs, and a capstone project simulating real-world scenarios.

Tool-Agnostic & Industry-Relevant: Covers a wide array of current tools like Copilot, ChatGPT, Gemini, Cursor, and more.

Career Boosting: Add in-demand AI automation skills to your resume.

Certificate of Completion: Demonstrate your applied understanding and practical competence in GenAI-assisted development.

Course Syllabus

Introduction to SDLC & GenAI Role

Overview of SDLC phases: Planning, Requirements, Design, Development, Testing, Deployment, and Maintenance.

Discussion on the role of Generative AI (GenAI) in transforming software development practices.

Include real-world case studies on successes and failures of SDLC with AI augmentation.

Students discuss impacts of GenAI on traditional software workflows and identify stages most changed by AI.

GenAI Evolution & Key Concepts

Explore history and evolution of GenAI in software engineering: from rule-based automation to modern LLMs (e.g., GPT series) and AI-assisted coding.

Cover concepts like natural language understanding, code synthesis, and automation tools. Highlight impact on productivity and innovation.

SDLC Roles and Artifacts

Detailed explanation of roles (analyst, developer, tester, maintainer) and artifacts (requirements docs, design specs, test plans) in SDLC.

Discuss how GenAI can support each role.

Group activity: map AI tools to corresponding SDLC phases and roles.

Installation & Setup: Tools Overview

Students install and configure essential tools for the course: Python, Git/GitHub, text editors (VS Code), Jupyter Notebooks/Colab, and GenAI API clients (OpenAI API).

Overview of basics for project collaboration and documentation. Validate environment with simple setup scripts.

Independent Research & Task

Task: Students research one GenAI tool (ex: GitHub Copilot, ChatGPT) and prepare a one-page summary outlining features, pros/cons, and integration in SDLC.

Optional: set up personal GitHub repos for code sharing and version control practice.

Agile & GenAI-Powered Project Management

Hands-on introduction to AI-powered project management tools (Jira AI, Notion AI).

Practice creating user stories, sprint backlogs, and task boards integrating AI assistance.

Discussion of ethical considerations in AI task automation.

GenAI APIs & Platforms Overview

Overview of popular GenAI platforms/APIs (OpenAI, LangChain, GitHub Copilot, Cursor.ai, Windsurf).

Students explore documentation, authentication setup, and basic API usage examples.

Lab Setup: GenAI API Integration

Guided setup of Python/JavaScript environments to interact with GenAI APIs.

Write scripts to send minimal queries and retrieve generated responses.

Practical tips on debugging and handling API limits/errors.

Independent Research & Tasks

Task: Students experiment with simple GenAI prompts for code generation, debugging, or documentation.

Document findings and best prompt engineering practices in shared notebooks or repos.

Planning Phase & Requirement Elicitation

Explore project planning processes in SDLC.

Use GenAI tools to automate requirements capture via NLP techniques: user stories, sentiment analysis, conflict detection.

Hands-on project: create Gantt charts and resource plans augmented with AI insights.

GenAI-Assisted Requirements Documentation

Guide on integrating GenAI to generate, review, and refine requirements documentation.

Exercise: authorship of sample requirements with AI-generated outlines and enhancements.

Discuss ethical data handling in requirements.

Feasibility & Risk Analysis

Use AI tools to assess project feasibility and identify risks.

Students simulate risk assessments augmented by AI-generated reports.

Discussion of how AI can help in early detection of project pitfalls.

Lab: GenAI Plugins for Planning Tools

Setup and configuration of GenAI-powered plugins/extensions for popular planning software (e.g., JIRA, MS Project).

Practice automating sprint planning and risk documentation.

Independent Study & Research

Task: Students research and prepare a comparative review of two GenAI tools for requirement elicitation/planning phases, with pros and cons, real application scenarios.

Design Phase & GenAI in UI/UX

Study use of GenAI tools (Figma AI, Uizard) for UI/UX design and prototyping.

Practical session on generating wireframes and design mockups using AI assistance.

Architecture Diagramming with GenAI

Explore how GenAI can produce and validate architecture diagrams from textual designs.

Demo using example UML and flowcharts with AI tools.

AI-Enabled Accessibility Design

Cover AI techniques to enhance accessibility: automated testing for WCAG compliance, voice UI prototyping, and AI-powered user feedback analysis.

Lab: GenAI-assisted Software Design

Hands-on session generating design documents and diagrams from project descriptions using GenAI tooling integrated with VS Code or web platforms.

Independent Assignments

Task: Students create a mini design document for a sample app using GenAI tools, then peer review and refine based on feedback.

Development Phase Overview

Discuss coding practices in modern SDLC; how GenAI assists code autocomplete, generation, refactoring, and debugging.

Students learn best practices integrating AI helpers responsibly.

Introduction to GitHub Copilot & Similar Tools

Demonstrate AI-based code assistance with GitHub Copilot and others.

Students get hands-on experiences writing code with autocomplete, code suggestions, and explanation features.

GenAI for Secure & Ethical Coding

Address AI-assisted generation of secure code, detecting vulnerabilities, and avoiding bias.

Case studies of AI code introducing bugs and mitigations.

Development Lab: AI-Powered Coding & Debugging

Students practice coding tasks with AI assistance in Python/JavaScript.

Debug and refactor generated code.

Document learnings and become familiar with prompt engineering for coding tasks.

Independent Research & Task

Task: Explore AI-generated code quality on independent small projects; write reflection notes and create GitHub repos showcasing experiments.

Testing Phase with GenAI

Introduction to automated test case generation using GenAI tools such as ChatGPT, LangChain.

Discuss benefits and limitations in unit, integration, and UI testing automation.

Automated Bug Triage & Reports

Explore AI techniques for bug triaging and summarizing issue reports.

Practice writing natural language descriptions of bugs and generating debugging steps.

GenAI-Driven UI Testing & Selenium

Introduction to Selenium automation integrated with GenAI-generated test scripts for UI testing workflows.

Hands-on generation and execution of AI-generated Selenium scripts.

Lab: Testing Pipeline Automation

Students build a test automation pipeline using AI-assisted test generation, run tests, analyze failures, and generate reports in Jupyter/Colab or IDEs.

Independent Work & Paper Review

Task: Research recent academic or industry papers on AI-enhanced software testing or QA techniques; present summaries and discuss applications.

Advanced AI-Powered Coding Tools

Deep dive into AI coding assistants like GitHub Copilot, Windsurf, Cursor.ai, and AWS CodeWhisperer.

Explore features such as contextual code completion, intelligent refactoring, and auto-documentation

Discuss integration in IDEs (VS Code, JetBrains).

Hands-on: practice complex multi-file scaffolding with AI support.

AI-Driven Secure & Ethical Coding

Explore techniques for generating secure code with GenAI, identifying common risks like injection flaws or insecure defaults.

Case discussions on prevention of bias and ethical pitfalls in AI-generated code.

Practice: Static analysis tools combined with AI models to review generated code.

Code Generation in Multiple Languages

Cover AI-supported coding in Python, JavaScript, and Java. Comparative review of AI support and challenges per language

Hands-on: multilingual coding exercises generating modules, APIs, and tests with AI help. Students reflect on language nuances.

Debugging & Refactoring with AI

Hands-on application of GenAI for automatic debugging tips, error explanation, and code refactoring.

Students experiment with prompt engineering to improve code maintainability.

Pair programming and peer review to reinforce concepts.

Independent Exploration & Repo Setup

Task: Setup personal project repos integrating AI-assistants.

Students document experiences using AI for code writing and debugging.

tart building a project that will evolve throughout the course with GenAI support logged via commits and issues.

Automated Test Case Generation

Overview of using GenAI tools to generate unit, integration, and regression test cases automatically.

Hands-on prompts for crafting effective test scenarios with ChatGPT and LangChain frameworks.

Discuss limits and correctness assurance.

AI for Bug Detection & Triage

Techniques to automate bug triage, generating issue tickets, and categorizing bugs using natural language generated summaries.

Practice creating reproducible bug reports with GenAI assistance.

Selenium Automation & GenAI Scripts

Integrating GenAI with Selenium for UI testing automation.

Create AI-generated Selenium scripts from textual test descriptions and refine based on test results.

Hands-on lab executing and debugging scripts. Discuss scaling automated tests in CI/CD pipelines.

AI-Powered Test Reporting

Automatic generation of test coverage and results summaries, anomaly detection in test runs.

Students build basic dashboards and automate reports using LLMs and visualization libraries.

Independent Research & Presentation

Students review recent advances in AI-powered QA/testing from papers or blogs.

Prepare short presentations summarizing innovations and practical benefits.

CI/CD Concepts & GenAI Integration

Review continuous integration and deployment workflows.

Explore how GenAI can assist in automating build, test, and deployment scripts.

Lab: Create basic CI/CD pipelines augmented with AI-generated shell or YAML files (GitHub Actions, Jenkins).

Containerization & Orchestration

Introduction to Docker and Kubernetes fundamentals

Practice building container images for AI-enhanced apps.

Use AI tools to generate Dockerfiles and Kubernetes manifests with best practices for reproducibility and scalability.

Automated Release Notes & Monitoring

Generate release notes automatically with GenAI based on commit history and issue trackers.

Discuss AI-powered monitoring tools (AIOps) for health checking and auto-remediation following deployment.

Hands-on: Setup alerts and generate incident summaries with AI.

Security & Compliance in Deployment

Discuss deployment security including secrets management, vulnerability scanning, and compliance checks aided by AI code review tools

Emphasis on best practices and continuous auditing.

Case studies on failures and lessons learned.

Independent Lab & Documentation

Students create end-to-end demo deployment pipelines with AI-generated config and scripts

Document the setup, challenges, and lessons learned in shared repos/wiki pages.

Post-Deployment Maintenance

Cover AI-driven concepts like self-healing systems, anomaly detection in logs, and automated patching.

Use case discussions and tools overview.

Incident Management & Reporting

Automate incident ticket generation and prioritization through GenAI.

Hands-on: Build scripts to extract actionable reports from monitoring logs.

Chatbots & Virtual Assistants

Introduction to building chatbots with OpenAI GPT, Dialogflow, and Rasa.

Students design and implement simple support bots for ticket routing and FAQs.

Review privacy and ethical considerations.

Sentiment Analysis & Feedback

Use AI tools to analyze sentiment in customer interactions to prioritize support efforts.

Practice on sample chat logs and call transcripts.

Independent Project Work & Sharing

Students expand on maintenance bots or monitoring scripts developed, share in project groups for feedback and collaborative improvement.

AI Explainability & Trust

Study the importance of transparent AI-generated code and decisions.

Discuss tools and techniques for explainable AI in software engineering, including model interpretability for code suggestions and testing.

Ethics, Bias & Responsible AI

Deep dive into ethical considerations in AI-assisted software development: data privacy, bias mitigation, accountability, and governance.

Engage students in scenario-based discussions and reflections.

Collaboration Tools & Agile with AI

Usage of AI-powered Agile tools to manage team collaboration, sprint planning, and retrospectives, including automated meeting notes and action items generation.

Simulations of AI-augmented scrum ceremonies.

Code Review Automation

Implement AI-assisted code review workflows to flag bugs, style issues, and vulnerabilities.

Practice setting up automated reviews and interpreting AI feedback in pull requests.

Research & Innovation Sharing

Students investigate emerging GenAI developments in SDLC, produce reports on promising tools or academic research, and lead discussions on future impacts.

Capstone Project Kickoff & Scoping

Teams finalize project topics applying GenAI to an SDLC phase.

Write detailed proposals with objectives, milestones, and deliverables.

Instructor-led guidance provided.

Literature Review & Data Collection

Conduct research on related solutions and datasets.

Evaluate feasibility and risks.

Prepare documentation.

Environment Setup & Tooling

Configure project environments: repos, CI/CD pipelines, API keys, and necessary software tools.

Setup performance and logging solutions from the start.

Initial Development & Testing

Begin core development with GenAI tools

Initial code commits and integration with AI assistants.

Begin first test cases and peer review cycles.

Team Collaboration & Feedback

Conduct sprint planning, daily stand-ups, and review sessions with AI-generated agendas and summary notes.

Collect early feedback and iterate project scope.

Model & System Development

Continue iterative project development, modelling, integration, and testing.

Emphasize documentation and AI-assisted code quality improvements

Advanced Evaluation & Refinement

Perform thorough testing: unit, integration, and user acceptance tests, fix issues using AI tools

Analyze test results, system performance.

Deployment Preparation & Demo Planning

Finalize deployment scripts, environment specs, and demo presentations.

Practice storytelling and technical demos for non-technical stakeholders.

Soft Skills Workshop

Workshops on presentation skills, conflict resolution, stakeholder communication, and team management.

Role-play scenarios and peer feedback sessions.

Independent Research & Exploration

Encourage experimentation with novel GenAI tools or integration methods related to the capstone project.

Prepare short reflection papers or demos to present.

Final Project Refining & Documentation

Complete project coding, comprehensive documentation, test scripts, and deployment configurations.

Ensure reproducibility of all results and demos.

Mock Presentations & Peer Review

Conduct dry runs of final presentations with peer and instructor feedback; refine slides, demos, and Q&A preparedness.

Career & Resume Workshops

Guidance on building AI/SDLC-related resumes, LinkedIn profiles, and job interview preparation.

Conduct mock technical interviews and coding challenges.

Technical Writing & Publication

Best practices for writing technical reports, papers, and open-source documentation

Create blogs or GitHub READMEs summarizing key project learnings.

Course Reflection & Next Steps

Group reflection on course journey, lessons learned, and pathways for further study or open-source/community contributions. Gather course feedback.

Capstone Project Final Presentations

Formal presentations and demonstrations to faculty and peers.

Q&A sessions and evaluation based on technical merit, innovation, and clarity.

Course Recap & Key Learnings

Review of core topics, key insights on GenAI in SDLC, and student achievements.

Discussion of ongoing trends and career opportunities.

Industry Expert Guest Session

Invited industry expert(s) share experiences using GenAI in real-world software projects.

Panel Q&A on career, skills, and future directions.

Written & Coding Assessments

Final course assessments: coding challenges, written reflections or quizzes evaluating comprehension and application of GenAI in SDLC.

Course Closure & Feedback Session

Collect feedback for course improvements, distribute certificates, and provide guidance on advanced learning resources like advanced AI courses, open-source contributions, or AI competitions.

Instructor Profile

Shyam Prakash

Shyam Prakash brings over 20 years of comprehensive product engineering experience to AI-assisted coding training, with deep expertise spanning the entire product lifecycle from conception to delivery. As a seasoned full-stack developer proficient in modern technologies, he has successfully led development teams and delivered applications serving thousands of active users across multiple startups and enterprises. His leadership journey includes co-founding and serving as CTO of Whatifics, scaling engineering teams at NDS Pay TV Technology, and driving agile transformations at Cisco Video Technologies. Having spent the past year actively practicing and refining AI-assisted coding workflows, Shyam combines his extensive hands-on development experience with practical insights into how AI tools can enhance engineering productivity, making him uniquely positioned to guide engineers in adopting and mastering AI-powered development practices.

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

Certification Criteria: Minimum 75% score required
Certificate Type: Digital e-certificate (no hard copies)