Course Outline
Day 1 Outline
Module 1 — Introduction to Claude Code & AI-Assisted Engineering
• Comparison of Claude Code and traditional AI tools
• Role of AI agents in software engineering
• Productivity and workflow optimisation
• AI-assisted development lifecycle
• Risks, limitations, and the importance of human oversight
• Live practical demonstrations
Module 2 — Prompt Engineering Fundamentals
• Anatomy of an effective prompt
• Zero-shot vs few-shot prompting
• Iterative prompting techniques
• Basics of prompt chaining
• Structured outputs and formatting
• Prompt verification and quality improvement
Module 3 — Prompting for Software Development
• Code generation and refactoring
• Debugging with AI assistance
• Documentation generation
• Pull request reviews
• Understanding legacy code
• Ensuring safe and maintainable AI-generated code
Module 4 — Prompting for Testing & Quality
• Test case generation
• Edge-case analysis
• Automation-ready test design
• AI-assisted defect analysis
• Creating Gherkin and test scenarios
• Quality verification workflows
Module 5 — Prompting for Agile Collaboration
• User stories and acceptance criteria
• Requirements refinement
• Supporting agile communication
• Preparing stakeholder summaries
• Assisting with retrospectives
• Backlog refinement preparation
Module 6 — Responsible AI, Security & Verification
• Addressing hallucinations and AI risks
• Maintaining confidentiality and secure prompting
• Principles of AI governance
• Using verification checklists
• Awareness of prompt injection threats
• Understanding human review responsibilities
Module 7 — Team Prompt Lab
• Building reusable team prompts
• Creating role-specific AI workflows
• Sharing prompts and peer review processes
• Developing a Team Prompt Library v1
• Engaging in interactive collaborative exercises
Day 2
Module 1 — Claude Code Advanced Capabilities
• Utilising CLAUDE.md and persistent project context
• Automating AI workflows
• Best-of-N generation strategies
• Creating reusable AI commands
• Techniques for context engineering
• Implementing AI-assisted engineering workflows
Module 2 — Advanced Prompt Engineering Techniques
• Chain-of-thought prompting
• Multimodal prompting
• Constraint-based prompting
• Advanced prompt chaining
• Managing large contexts
• Developing conversational engineering workflows
Module 3 — Version Control, Parallel Development & Multi-Agent Workflows
• Strategies for Git integration
• Parallel AI development workflows
• Using worktrees and isolated AI tasks
• Orchestrating multi-agent systems
• Implementing 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 processes with AI
• Managing incident postmortems and operational workflows
Module 5 — Scaling Claude Code & Codebase Health
• Managing tokens and context
• Structuring projects for AI friendliness
• Ensuring long-term codebase maintainability
• Automating documentation
• Developing AI scalability strategies
• Establishing team-wide engineering workflows
Module 6 — Capstone: Define Your Claude Code Process
• Designing scalable AI-assisted workflows
• Combining prompts, commands, and context files
• Structuring team AI processes
• Establishing cross-role collaboration models
• Creating workflow blueprints
Module 7 — Advanced Team Prompt Lab
• Developing advanced prompt libraries
• Designing complex role-specific workflows
• Validating prompts in real-world scenarios
• Facilitating cross-team collaboration exercises
• Finalising Team Prompt Library v2
Requirements
Day 1 — Foundation
• Basic familiarity with 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 experience)
• Prior exposure to Claude Code and prompt engineering concepts
• Basic Git knowledge
• Familiarity with CI/CD concepts is recommended
