AI-Assisted Software Development with GitHub Copilot

Introduction to AI-Assisted Development

• Why AI is revolutionizing software development and its career impact
• General introduction to Large Language Models (LLMs)
• Conceptual overview of how LLMs work with code
• Capabilities and limitations of LLMs in coding contexts
• Software engineering practices vs “vibe coding”
• Essential do's and don'ts for AI-assisted development

Enterprise Adoption and Governance

• Organizational benefits: accelerated development, improved documentation, enhanced testing
• Risk assessment: IP leakage, code quality, developer overreliance
• Governance and compliance considerations
• Data protection and licensing safeguards
• Deployment options comparison (Individual vs Business vs Enterprise plans)
• Best practices for safe organizational use

Practical Implementation

• GitHub Copilot overview and VS Code integration
• Understanding instructions vs prompts vs custom chat modes
• Installation, configuration, and team setup
• Working with different Copilot modes (Ask, Edit, Agent, Custom)
• Exercise #1: Forking course repositories
• Exercise #2: Building a simple calculator with progressive features using test-driven development
• Real-world coding scenarios and prompt engineering

Monday lunch break

Consolidation and Next Steps

• Review of learning outcomes
• Open Q&A session
• Future learning opportunities and resources
• Homework assignment: Finding and preparing a legacy codebase for continued practice

Understanding Legacy vs. Evergreen Code

• What defines legacy code (and what doesn't)
• Why codebases degrade over time
• The evergreen code philosophy: “If we rewrote it today, it would look exactly like this”
• Building respect, not fear, for working systems

Pre-AI Checklist: Essential Safety Measures

• Backup and rollback strategies (branching, commits, archiving)
• Testing confidence frameworks
• Change review processes
• Incremental change methodology

Managing GitHub Copilot Effectively

• Treating Copilot as a managed junior developer
• Understanding context windows and token limitations
• Prompt design best practices
• Model selection strategies (GPT-4o, Claude Sonnet, etc.)

Advanced Context Techniques

• File and folder mentions (#-syntax)
• Spaces and Knowledge Bases integration
• Premium usage monitoring and optimization
• Token estimation and context overflow detection

Tuesday lunch break.

Documentation Generation and Code Analysis

• Automated README and documentation updates
• Architecture diagram generation
• Complex code explanation and mapping
• Identifying technical debt hotspots

Hands-On Exercise: Legacy Code Documentation

• Generate development and deployment guides
• Create architecture diagrams
• Update project documentation
• Cross-validate with multiple AI models

Test Automation and Code Quality

• AI-assisted test generation (unit, integration, E2E)
• Intelligent linting beyond static analysis
• Coverage analysis and test adequacy assessment
• Automated quality gates

Creating Robust Testing Frameworks

• Generating comprehensive test suites
• Test review and validation strategies
• Balancing test coverage with maintainability

Compliance and Gap Analysis

• Comparing implementations against instruction files
• Automated issue generation from compliance gaps
• Prioritizing technical debt remediation
• Creating actionable remediation plans

Multi-Model Implementation Comparison

• Implementing changes with different AI models
• Comparing approaches and outcomes
• Risk assessment and quality evaluation
• Best practice synthesis

Wednesday lunch break.

• High-level workflow from requirements to solution
• Role of stakeholders and AI in each phase
• Process visualization and workflow diagrams

• Defining business rules and workflows
• Technology stack and architecture decisions
• Introduction to Zeus.Academia.2 project example
• Conceptual models and Object Role Models
• Use cases and data capture scenarios

Exercise: Create project requirement instructions

Thursday lunch break.

Instruction Files:

• Persistent behavioral guidelines for AI
• Coding standards and security policies
• Repository-wide consistency

Prompt Files:

• Executable task templates
• Structured workflows for specific tasks
• Repeatable automation patterns

• Layered integration approach
• When to use each method
• Real-world implementation examples
• Common pitfalls and how to avoid them

Friday lunch break.

• Creating custom chat modes with personas
• Generating implementation prompts and verification
• Agent configuration with safe commands
• Testing and validation workflows