Course Outline
Day 1 Outline
Module 1 — Introduction to Claude Code & AI-Assisted Engineering
• Comparing Claude Code with traditional AI tools
• The role of AI agents in software engineering
• Optimising productivity and workflows
• Integrating AI-assisted development lifecycles
• Addressing risks, limitations, and the necessity of human oversight
• Live practical demonstrations
Module 2 — Prompt Engineering Fundamentals
• Deconstructing an effective prompt
• Zero-shot vs. few-shot prompting strategies
• Techniques for iterative prompting
• Basics of prompt chaining
• Structured outputs and formatting standards
• Verifying prompts and enhancing output quality
Module 3 — Prompting for Software Development
• Code generation and refactoring
• Debugging with AI assistance
• Automated documentation generation
• Reviewing pull requests
• Comprehending legacy codebases
• Ensuring safe and maintainable AI-generated code
Module 4 — Prompting for Testing & Quality Assurance
• Generating test cases
• Analysing edge cases
• Designing automation-ready tests
• Conducting AI-assisted defect analysis
• Creating Gherkin syntax and test scenarios
• Establishing quality verification workflows
Module 5 — Prompting for Agile Collaboration
• Drafting user stories and acceptance criteria
• Refining requirements
• Supporting agile communication channels
• Preparing stakeholder summaries
‣ Facilitating retrospectives
• Preparing backlog refinement sessions
Module 6 — Responsible AI, Security & Verification
• Identifying hallucinations and mitigating AI risks
• Ensuring confidentiality through secure prompting
• Understanding AI governance principles
• Utilising verification checklists
• Recognising prompt injection threats
• Defining human review responsibilities
Module 7 — Team Prompt Lab
• Constructing reusable team prompts
• Designing role-specific AI workflows
• Sharing prompts and conducting peer reviews
• Creating Team Prompt Library v1
• Participating in interactive collaborative exercises
Day 2
Module 1 — Claude Code Advanced Capabilities
• Utilising CLAUDE.md for persistent project context
• Automating AI workflows
• Implementing best-of-N generation strategies
• Developing reusable AI commands
• Applying context engineering techniques
• Optimising AI-assisted engineering workflows
Module 2 — Advanced Prompt Engineering Techniques
• Employing chain-of-thought prompting
• Exploring multimodal prompting
• Using constraint-based prompting
• Mastering advanced prompt chaining
• Managing large contexts
• Navigating conversational engineering workflows
Module 3 — Version Control, Parallel Development & Multi-Agent Workflows
• Implementing Git integration strategies
† Enabling parallel AI development workflows
† Leveraging worktrees for isolated AI tasks
† Orchestrating multi-agent systems
† Establishing human-in-the-loop checkpoints
† Managing conflict resolution strategies
Module 4 — Architecture, MCP & Advanced DevOps
• Understanding the Model Context Protocol (MCP)
• Integrating Claude with external tools
• Conducting AI-assisted architecture analysis
• Documenting Architecture Decision Records (ADR)
• Troubleshooting CI/CD pipelines with AI
• Conducting incident postmortems and managing operational workflows
Module 5 — Scaling Claude Code & Codebase Health
• Managing tokens and context limits
• Structuring projects for AI compatibility
• Ensuring long-term codebase maintainability
• Automating documentation processes
• Developing AI scalability strategies
• Implementing team-wide engineering workflows
Module 6 — Capstone: Define Your Claude Code Process
• Designing scalable AI-assisted workflows
• Integrating prompts, commands, and context files
• Formulating team AI processes
• Establishing cross-role collaboration models
• Creating comprehensive workflow blueprints
Module 7 — Advanced Team Prompt Lab
• Developing advanced prompt libraries
• Executing complex role-specific workflows
• Validating prompts in real-world scenarios
• Engaging in cross-team collaboration exercises
• Finalising Team Prompt Library v2
Requirements
Day 1 — Foundation
• Foundational knowledge of software delivery processes
• General understanding of development, testing, or agile workflows
• Access to Claude is recommended for hands-on exercises
Day 2 — Advanced
• Completion of Day 1 (or equivalent professional experience)
• Prior exposure to Claude Code and prompt engineering concepts
• Basic proficiency in Git
• Familiarity with CI/CD concepts is advantageous
