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Course Outline
⚔️ Level 1: The Discovery Dungeon – Secrets of Requirements
Mission: Use LLMs (ChatGPT) to extract structured requirements from vague input.
Key Activities:
- Interpret ambiguous product ideas or feature requests
- Use AI to:
- Generate user stories and acceptance criteria
- Suggest personas and scenarios
- Generate visual artifacts (e.g., simple diagrams with Mermaid or draw.io)
Outcome: Structured backlog of user stories + initial domain model/visuals
🔥 Level 2: The Design Forge – Architect’s Scroll
Mission: Use AI to create and validate architecture plans.
Key Activities:
- Use AI to:
- Propose architectural style (monolith, microservices, serverless)
- Generate high-level component and interaction diagrams
- Scaffold class/module structures
- Challenge each other's choices through peer design reviews
Outcome: Validated architecture + code skeleton
🧙♂️ Level 3: The Code Arena – Codex Gauntlet
Mission: Use AI copilots to implement features and improve code.
Key Activities:
- Use GitHub Copilot or ChatGPT to implement functionality
- Refactor AI-generated code for:
- Performance
- Security
- Maintainability
- Inject “code smells” and run peer clean-up challenges
Outcome: Functional, refactored, AI-generated codebase
🐛 Level 4: The Bug Swamp – Test the Darkness
Mission: Generate and improve tests with AI, then find bugs in others’ code.
Key Activities:
- Use AI to generate:
- Unit tests
- Integration tests
- Edge case simulations
- Exchange buggy code with another team for AI-assisted debugging
Outcome: Test suite + bug report + bug fixes
⚙️ Level 5: The Pipeline Portals – Automaton Gate
Mission: Set up smart CI/CD pipelines with AI assistance.
Key Activities:
- Use AI to:
- Define workflows (e.g., GitHub Actions)
- Automate build, test, and deploy steps
- Suggest anomaly detection/rollback policies
Outcome: AI-assisted, working CI/CD pipeline script or flow
🏰 Level 6: The Monitoring Citadel – Watchtower of Logs
Mission: Analyze logs and use ML to detect anomalies and simulate recovery.
Key Activities:
- Analyze pre-populated or generated logs
- Use AI to:
- Identify anomalies or error trends
- Suggest automated responses (e.g., self-healing scripts, alerts)
- Create dashboards or visual summaries
Outcome: Monitoring plan or simulated intelligent alerting mechanism
🧙♀️ Final Level: The Hero’s Arena – Build the Ultimate AI-Supported SDLC
Mission: Teams apply everything learned to build a working SDLC loop for a mini-project.
Key Activities:
- Select a team mini-project (e.g., bug tracker, chatbot, microservice)
- Apply AI at each SDLC phase:
- Requirements, Design, Code, Test, Deploy, Monitor
- Present outcomes in a short team demo
Peer voting or judging for most effective AI-powered pipeline
Outcome: End-to-end AI-enhanced SDLC implementation + team showcase
By the end of this workshop, participants will be able to:
- Apply generative AI tools to extract and structure software requirements
- Generate architectural diagrams and validate design choices using AI
- Use AI copilots to implement and refactor production-grade code
- Automate test generation and perform AI-assisted debugging
- Design intelligent CI/CD pipelines that detect and react to anomalies
- Analyze logs with AI/ML tools to identify risks and simulate self-healing
- Demonstrate a fully AI-enhanced SDLC through a mini team project
Requirements
Audience: Software developers, testers, architects, DevOps engineers, product owners
Participants should have:
- A working understanding of the Software Development Lifecycle (SDLC)
- Practical experience in at least one programming language (e.g., Python, Java, JavaScript, C#, etc.)
- Familiarity with:
- Writing and reading user stories or requirements
- Basic software design principles
- Version control (e.g., Git)
- Writing and executing unit tests
- Running or interpreting CI/CD pipelines
💡 This is an intermediate-to-advanced workshop. It's ideal for professionals who are already part of software delivery teams (developers, testers, DevOps engineers, architects, product owners).
7 Hours
