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Course Outline

Day 1 Outline

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

• Comparison of Claude Code with traditional AI tools
• The role of AI agents in software engineering
• Optimising productivity and workflows
• 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
• Fundamentals of prompt chaining
• Structured outputs and formatting
• Verifying prompts and improving quality

Module 3 — Prompting for Software Development

• Code generation and refactoring
• Debugging with AI assistance
• Automated 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 syntax and test scenarios
• Quality verification workflows

Module 5 — Prompting for Agile Collaboration

• Drafting user stories and acceptance criteria
• Requirements refinement
• Supporting agile communication
• Generating stakeholder summaries
• Assisting with retrospectives
• Preparing for backlog refinement

Module 6 — Responsible AI, Security & Verification

• Understanding hallucinations and AI risks
• Ensuring confidentiality and secure prompting
• Principles of AI governance
• Verification checklists
• Awareness of prompt injection threats
• Human review responsibilities

Module 7 — Team Prompt Lab

• Building reusable team prompts
• Developing role-specific AI workflows
• Sharing prompts and conducting peer reviews
• Creating the first version of the Team Prompt Library
• Engaging in interactive collaborative exercises

Day 2

Module 1 — Claude Code Advanced Capabilities

• Leveraging CLAUDE.md for persistent project context
• Automating AI workflows
• Best-of-N generation strategies
• Creating reusable AI commands
• Context engineering techniques
• Integrating 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
• Conversational engineering workflows

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

• Git integration strategies
• Parallel AI development workflows
• Using worktrees and isolated AI tasks
• Multi-agent orchestration
• Human-in-the-loop checkpoints
• Conflict management strategies

Module 4 — Architecture, MCP & Advanced DevOps

• Understanding the Model Context Protocol (MCP)
• Integrating Claude with external tools
• AI-assisted architecture analysis
• Creating Architecture Decision Records (ADR)
• AI-assisted CI/CD troubleshooting
• Conducting incident postmortems and operational workflows

Module 5 — Scaling Claude Code & Codebase Health

• Managing tokens and context effectively
• Designing AI-friendly project structures
• Ensuring long-term codebase maintainability
• Automating documentation
• Implementing 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
• Developing team AI processes
• Defining cross-role collaboration models
• Creating workflow blueprints

Module 7 — Advanced Team Prompt Lab

• Developing advanced prompt libraries
• Building complex role-specific workflows
• Validating prompts in real-world scenarios
• Engaging in cross-team collaboration exercises
• Finalising the Team Prompt Library v2

Requirements

Day 1 — Foundation

• Basic familiarity with software delivery processes
• General understanding of development, testing, or agile workflows
• Claude access 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

 14 Hours

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