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AI-Assisted Legacy (Brownfield) Software Development with GitHub Copilot

Revolutionize your software development with GitHub Copilot in this comprehensive remote training for developers, technical leads, and engineering managers. Over five half-day sessions, you will gain hands-on experience mastering AI-assisted coding fundamentals and safe legacy code modernization. Learn to implement enterprise governance, enhance code quality, and boost productivity through practical AI workflows, automated testing, and prompt engineering techniques. Whether you’re looking to modernize existing systems or deliver new features, this class equips you with essential strategies and templates to harness AI effectively while managing risk and technical debt. Register for this course to transform your team’s software engineering approach and stay ahead in the AI-powered development revolution.

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Sign up today and save $ 300!  Only $ 1,300!
(Early bird price is only good until 03/02/2026)

Transform Your Development Workflow with GitHub Copilot and Advanced AI Tools Master the future of software engineering through a comprehensive training class designed for modern development teams.

  • Duration: Five half-days of 4 hours each (20 hours total). Note that hours are shown in the Central time zone.
  • Delivery: Remote (instructor in California; students distributed, class size 6–20)
  • Audience: Software developers who are familiar with GitHub and Copilot novices

Who Should Attend

Software developers, technical leads, and engineering managers ready to leverage AI for accelerated development, legacy system modernization, and greenfield project creation. You should have some experience using ChatGPT, Claude, or a similar LLM, plus some familiarity with GitHub Copilot and software development fundamentals.

What You'll Master

AI-Assisted Development Fundamentals (1 day) Break into AI-powered coding with GitHub Copilot. Learn enterprise governance, hands-on implementation across different Copilot modes, and software engineering best practices that maintain code quality while dramatically boosting productivity.

Legacy Code Transformation (4 days) Master safe legacy code modification, automated documentation generation, AI-assisted testing, and incremental modernization strategies that reduce technical debt while minimizing risk.

Why This Training Matters The AI development era is happening now. Developers using AI tools report 30–50% productivity gains, but only when used correctly. This training teaches you to harness AI as a force multiplier while avoiding common pitfalls that lead to poor code quality and technical debt.

You'll gain:

  • Immediate productivity improvements through proper AI tool usage
  • Enterprise-ready governance frameworks for team adoption
  • Risk management strategies for production environments
  • Template libraries and workflows for ongoing success

Cost Matrix Price grid

What's the Tech Stack for this Class?

  • IDE: VS Code with the GitHub Copilot Extension
  • SCC: GitHub
  • Coding Languages: C#, TypeScript, Vue.js
  • Tooling: Mob.sh

NOTE: The emphasis is on AI assistance and how to use AI effectively to generate code. The techniques are implementable in any tech stack.

Training 5+ developers? Let's customize it.

Our AI-assisted development techniques work with any tech stack—but they're even more powerful when taught using your actual tools and project types. We'll tailor the class to your team's real-world needs.

Reach out to discuss a custom program.

At a Glance:

Monday, March 23, 2026 - 9:00AM to Friday, March 27, 2026 - 5:00PM (all times US Central Time)
Price: $1,600
Sign up today and save $ 300!  Only $ 1,300!
(Early bird price is only good until 03/02/2026)

Agenda:

MONDAY

Introduction to AI-Assisted Development

Understanding Large Language Models

Understanding LLM Code Generation

Enterprise Adoption and Governance

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

Getting Hands-On with Copilot

  • Installation, configuration, and team setup
  • Using Copilot in different modes
  • IDE support for AI assistance

Mob Programming

  • What is mob programming and why are we using it?
  • What is mob.sh and what problem does it solve?
  • Installing, configuring, and using mob.sh
  • Best practices

Collaborating on a Solution

  • Project setup
  • Adding features
  • Test automation
  • Dependency management
  • Comparing implementations
  • Chat management

TUESDAY-FRIDAY

*NOTE: Content will shift a bit (to include labs) depending on feedback of the attendees.*

Understanding Brownfield vs. Evergreen Code

Pre-AI Checklist: Essential Safety Measures

  • Backup and rollback strategies (branching, commits, archiving)
  • Testing confidence frameworks
  • Change review processes
  • Incremental change methodology
  • Keeping change sets small
  • Respecting brownfield code

Managing GitHub Copilot Effectively

Advanced Context Techniques

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

Adding AI Guardrails

  • What are instructions, prompts, and chat modes?
  • Creating instruction, prompt, and chat mode files
  • Meta prompts that create instruction, prompt, and chat mode files
  • Instructions for generating artifacts
  • Enforcing provenance for all AI-assisted artifacts

Documentation Generation and Code Analysis

  • Automated README and documentation updates
  • Architecture diagram generation
  • Complex code explanation and mapping
  • Identifying technical debt hotspots
  • Generate development and deployment guides
  • Create architecture diagrams
  • Update project documentation
  • Cross-validate with multiple AI models

Building a Backlog

  • Identifying technical debt
  • Automating the creation of GitHub issues

Building Safety Nets

  • Protecting brownfield codebases
  • Leveraging AI code reviews
  • Effective human code reviews
  • The role of test automation

Test Automation and Code Quality

Creating Robust Testing Frameworks

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

Safe Brownfield Code Modification

  • Using feature flags to minimize risk
  • As Is and To Be test suites
  • Testing in production
  • Retiring feature flags

Compliance and Gap Analysis

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

Addressing Technical Debt

  • Prompting Copilot to address debt
  • Assigning issues to Copilot
  • What Copilot does with assigned issues

Multi-Model Implementation Comparison

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