Course Outline
Level 1: The Discovery Dungeon – Secrets of Requirements
Mission: Leverage LLMs (ChatGPT) to extract structured requirements from ambiguous input.
Key Activities:
- Interpret unclear product concepts or feature requests
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Utilize AI to:
- Create user stories and acceptance criteria
- Propose personas and use-case scenarios
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Produce visual artifacts (e.g., basic diagrams using Mermaid or draw.io)
Outcome: A structured backlog of user stories along with an initial domain model or visuals
Level 2: The Design Forge – Architect’s Scroll
Mission: Use AI to draft and validate architectural plans.
Key Activities:
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Employ AI to:
- Suggest architectural styles (monolith, microservices, serverless)
- Generate high-level component and interaction diagrams
- Scaffold class or module structures
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Challenge peer design choices through collaborative reviews
Outcome: Validated architecture and a code skeleton
Level 3: The Code Arena – Codex Gauntlet
Mission: Utilize AI copilots to implement features and refine code quality.
Key Activities:
- Implement functionality using GitHub Copilot or ChatGPT
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Refactor AI-generated code to enhance:
- Performance
- Security
- Maintainability
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Introduce code smells and conduct peer-led cleanup exercises
Outcome: A functional, refactored, AI-generated codebase
Level 4: The Bug Swamp – Test the Darkness
Mission: Generate and enhance tests with AI, then identify bugs in teammates’ code.
Key Activities:
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Use AI to generate:
- Unit tests
- Integration tests
- Edge case simulations
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Exchange buggy code with another team for AI-assisted debugging
Outcome: A test suite, bug report, and bug fixes
Level 5: The Pipeline Portals – Automaton Gate
Mission: Establish intelligent CI/CD pipelines with AI support.
Key Activities:
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Use AI to:
- Define workflows (e.g., GitHub Actions)
- Automate build, test, and deployment steps
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Suggest anomaly detection or rollback policies
Outcome: An AI-assisted, operational CI/CD pipeline script or flow
Level 6: The Monitoring Citadel – Watchtower of Logs
Mission: Analyze logs and apply ML to detect anomalies and simulate recovery.
Key Activities:
- Examine pre-populated or generated logs
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Use AI to:
- Identify anomalies or error trends
- Recommend automated responses (e.g., self-healing scripts, alerts)
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Create dashboards or visual summaries
Outcome: A monitoring plan or simulated intelligent alerting mechanism
Final Level: The Hero’s Arena – Build the Ultimate AI-Supported SDLC
Mission: Teams apply all learned concepts to construct a working SDLC loop for a mini-project.
Key Activities:
- Select a team mini-project (e.g., bug tracker, chatbot, microservice)
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Apply AI at each SDLC phase:
- Requirements, Design, Code, Test, Deploy, Monitor
- Present results in a concise team demo
Peer voting or judging for the most effective AI-powered pipeline
Outcome: An end-to-end AI-enhanced SDLC implementation and 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
Target Audience: Software developers, testers, architects, DevOps engineers, and product owners
Participants are expected to have:
- A solid grasp of the Software Development Lifecycle (SDLC)
- Practical proficiency in at least one programming language (e.g., Python, Java, JavaScript, C#, etc.)
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Familiarity with:
- Composing and interpreting user stories or requirements
- Fundamental software design principles
- Version control systems (e.g., Git)
- Creating and executing unit tests
- Managing or analyzing CI/CD pipelines
This workshop is geared toward intermediate-to-advanced professionals. It is particularly suitable for individuals already engaged in software delivery roles, including developers, testers, DevOps engineers, architects, and product owners.
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
