AI-READY
The AI-Ready Software Engineer: Spec-Driven Development with GitHub Copilot
How We Conduct Training :
O&B Headquarters (3F Carlos J. Valdes Building, 108 Aguirre, Legazpi Village, Makati City)
Client's premises (Private sessions available with a minimum number of participants)
Course Overview
As AI coding assistants become increasingly integrated into software engineering workfl ows, organizations must equip their teams with the skills to effectively and responsibly leverage these technologies. This course prepares software engineers to build modern applications using AI-assisted development practices, with a strong focus on spec-driven development and GitHub Copilot.
The course combines theory, hands-on workshops, and real-world implementation exercises using Python or Java as the primary programming language, depending on the client’s preference. Participants will learn how to integrate GitHub Copilot throughout the Software Development Lifecycle (SDLC), from requirements gathering and architecture design to implementation, testing, debugging, deployment, and governance.
The workshop emphasizes practical enterprise scenarios, including AI governance, secure coding, Retrieval-Augmented Generation (RAG), agentic AI workfl ows, prompt engineering, and local deployment strategies. Participants will gain a deeper understanding of how to work effectively with AI coding assistants while maintaining software quality, security, compliance, and maintainability.
Rather than simply teaching AI tools, this course develops a sustainable engineering methodology that enables teams to work faster, collaborate more effectively, and deliver higher-quality software with AI as a development accelerator.
Prerequisite Details
By the end of the course, participants will be able to:
- Understand the capabilities, limitations, and operational model of GitHub Copilot in software engineering workflows
- Apply advanced prompt engineering and context management techniques for AI-assisted development
- Implement spec-driven development practices using AI coding assistants
- Generate and refi ne requirements, API specifications, and BDD scenarios with AI assistance
- Build software features and services from formal specifications using GitHub Copilot
- Configure development environments for optimal AI-assisted productivity
- Use AI-assisted workflows for testing, debugging, code reviews, and version control
- Design AI-enabled applications using RAG and agentic AI patterns
- Apply AI governance, security, compliance, and responsible AI practices in enterprise environments
- Explore deployment strategies for AI-powered applications, including local and sovereign AI considerations
Course Outline
Day 1: Mastering GitHub Copilot for Spec-Driven Foundations
Module 1: GitHub Copilot Fundamentals & Advanced Prompt Engineering for Software
- Introduction to GitHub Copilot and AI-assisted software engineering
- Understanding LLM capabilities, limitations, and operational models
- Prompt engineering techniques for software development
- Context window management and token optimization
- Custom instructions and project-level AI guidance
- Few-shot prompting and chain-of-thought prompting
- Hands-on prompt engineering workshop
Module 2: Spec-Driven Development with GitHub Copilot – Requirements, Design & Context Management
- Principles of spec-driven development
- AI-assisted requirements gathering and clarification
- Generating API specifications and BDD scenarios
- Managing context for large specifications
- Configuring IDEs for GitHub Copilot integration
- Workshop: AI-assisted spec generation and feature prototyping
Day 2: GitHub Copilot for Implementation, Architecture, and Quality Assurance
Module 3: GitHub Copilot-Assisted Implementation & Architecture from Specifications
- Generating code from formal specifications
- Breaking down complex specifications into manageable prompts
- Integrating AI-generated components into existing architectures
- Designing applications with AI-powered features
- Performance optimization with GitHub Copilot assistance
- Workshop: Building and integrating a feature from specifications
Module 4: GitHub Copilot-Assisted Testing, Debugging & Version Control
- AI-assisted test generation and validation
- Identifying gaps between implementation and specifications
- AI-assisted debugging and root cause analysis
- Code review workflows using GitHub Copilot
- Git workflows and AI-generated commit messages
- Workshop: Testing, debugging, and code review activities
Day 3: Advanced AI Patterns, Governance, Security, and Local Deployment
Module 5: Advanced AI Patterns: RAG & Agentic AI with GitHub Copilot
- Introduction to Retrieval-Augmented Generation (RAG)
- Vector databases and semantic retrieval concepts
- Building AI agents and workflow automation
- Using agents.md for agent behavior definition
- Workshop: Building a simple RAG application or AI agent
Module 6: AI Governance, Security, and Sovereign AI
- Data privacy and compliance considerations
- Prompt injection risks and mitigation
- AI supply chain security
- Secure AI-assisted development practices
- Sovereign AI and local LLM deployment strategies
- Enterprise AI governance best practices
Module 7: Deployment Strategies, Monitoring, Responsible AI & Integrated Local Project
- Deployment approaches for AI-powered applications
- Local deployment with Streamlit or Flask
- Monitoring AI applications and model performance
- Responsible AI principles and bias mitigation
- Integrated applied exercise and security review
Module 8: Future Trends & Q&A
- Discussion on emerging trends in GitHub Copilot and AI for software engineering.
- Open Q&A and course wrap-up.
MACHINE PROBLEM & CODE REVIEW
Participants will complete guided hands-on workshops and implementation exercises throughout the training. In addition, participants are encouraged to complete a self-paced capstone project:
Capstone Project: “The Secure Internal Compliance & Knowledge Assistant”
Objective: Design and build a prototype of an internal AI assistant using GitHub Copilot, leveraging RAG, to help a hypothetical mid-market fi rm quickly access and understand its internal compliance documents and best practices. This project will challenge your assumptions about AI’s role in sensitive data environments and your ability to apply spec-driven development with security and governance in mind.
Scenario: Imagine you are a software engineer at a mid-market client. This client has numerous internal policy documents, legal guidelines, and operational best practices stored in various formats (PDFs, internal wikis, Word documents). Employees frequently struggle to fi nd specifi c information or understand complex compliance requirements. The client wants a secure, internal AI assistant that can answer questions based only on these approved internal documents, without hallucinating or exposing sensitive information.
The project includes:
- Spec-driven system design and documentation
- RAG pipeline implementation using local vector databases
- AI-assisted software development workflows
- Local deployment using Streamlit or Flask
- Security, governance, and compliance considerations
- Testing and validation of AI-generated outputs
Code review sessions and workshop feedback will focus on:
- Specification quality and alignment
- Prompt engineering effectiveness
- Code maintainability and readability
- Security and governance practices
- Testing completeness and validation strategies
- Responsible AI implementation
Software and Hardware Requirements
Hardware Requirements
- Laptop or desktop computer
- Stable internet connection
Software Requirements
- GitHub Copilot: As the primary AI coding assistant for all stages, leveraging custom instructions and agents.md.
- Python/Java: As the primary development language.
- Vector Database: ChromaDB (for local, in-memory or fi le-based storage) or a similar local vector store.
- Local UI Framework: Streamlit or Flask for a simple web interface.
- Git: For version control.