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 Duration 14 hours

Course Outline

Day 1 Outline

Module 1 — Introduction to Claude Code & AI-Assisted Engineering

• Claude Code compared to traditional AI tools
• The role of AI agents in software engineering
• Optimising productivity and workflows
• The AI-assisted development lifecycle
• Risks, limitations, and the need for human oversight
• Live practical demonstrations

Module 2 — Prompt Engineering Fundamentals

• The structure of an effective prompt
• Zero-shot versus few-shot prompting
• Iterative prompting methods
• Basics of prompt chaining
• Managing structured outputs and formatting
• Prompt verification and quality enhancement

Module 3 — Prompting for Software Development

• Code generation and refactoring
• AI-assisted debugging
• Automated documentation generation
• Pull request review support
• Interpreting legacy code
• Ensuring safe and maintainable AI-generated code

Module 4 — Prompting for Testing & Quality

• Test case generation
• Analysis of edge cases
• Designing automation-ready tests
• AI-assisted defect analysis
• Creating Gherkin and test scenarios
• Quality verification workflows

Module 5 — Prompting for Agile Collaboration

• Formulating user stories and acceptance criteria
• Refining requirements
• Supporting agile communication
• Creating stakeholder summaries
• Assisting with retrospectives
• Preparing for backlog refinement

Module 6 — Responsible AI, Security & Verification

• Addressing hallucinations and AI risks
• Maintaining confidentiality and secure prompting
• Principles of AI governance
• Implementation of verification checklists
• Awareness of prompt injection
• Defining human review responsibilities

Module 7 — Team Prompt Lab

• Developing reusable team prompts
• Designing role-specific AI workflows
• Sharing prompts and conducting peer reviews
• Creating Team Prompt Library v1
• Engaging in interactive collaborative exercises

Day 2

Module 1 — Claude Code Advanced Capabilities

• Using CLAUDE.md for persistent project context
• Automating AI workflows
• Implementing best-of-N generation strategies
• Creating reusable AI commands
• Techniques for context engineering
• Establishing 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
• Engineering conversational workflows

Module 3 — Version Control, Parallel Development & Multi-Agent Workflows

• Strategies for Git integration
• Parallel AI development workflows
• Utilising worktrees and isolated AI tasks
• Orchestration of multi-agent systems
• Human-in-the-loop checkpoints
• Strategies for conflict management

Module 4 — Architecture, MCP & Advanced DevOps

• The Model Context Protocol (MCP)
• Integrating Claude with external tools
• AI-assisted architecture analysis
• Documentation of Architecture Decision Records (ADR)
• Troubleshooting AI-assisted CI/CD
• Managing incident postmortems and operational workflows

Module 5 — Scaling Claude Code & Codebase Health

• Managing tokens and context
• Structuring projects for AI compatibility
• Ensuring long-term codebase maintainability
• Automating documentation
• Strategies for AI scalability
• Implementing team-wide engineering workflows

Module 6 — Capstone: Define Your Claude Code Process

• Designing scalable AI-assisted workflows
• Integrating prompts, commands, and context files
• Designing team AI processes
• Modelling cross-role collaboration
• Creating workflow blueprints

Module 7 — Advanced Team Prompt Lab

• Developing advanced prompt libraries
• Creating complex role-specific workflows
• Validating prompts in real-world scenarios
• Executing 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 knowledge of Git
• Familiarity with CI/CD concepts is recommended

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