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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
• Productivity and workflow optimization
• 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
• Fundamentals of prompt chaining
• Structured outputs and formatting
• Prompt verification and quality enhancement

Module 3 — Prompting for Software Development

• Code generation and refactoring
• Debugging with AI support
• 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
• Designing tests ready for automation
• 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
• Support for agile communication
• Stakeholder summaries
• Assisting with retrospectives
• Preparing for backlog refinement

Module 6 — Responsible AI, Security & Verification

• Hallucinations and AI-related risks
• Confidentiality and secure prompting practices
• Principles of AI governance
• Verification checklists
• Awareness of prompt injection
• Responsibilities for human review

Module 7 — Team Prompt Lab

• Creating reusable team prompts
• Role-specific AI workflows
• Prompt sharing and peer review
• Developing Team Prompt Library v1
• Interactive collaborative exercises

Day 2

Module 1 — Advanced Capabilities of Claude Code

• CLAUDE.md and maintaining persistent project context
• Automating AI workflows
• Best-of-N generation strategies
• Reusable AI commands
• Context engineering techniques
• 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
• Worktrees and isolated AI tasks
• Multi-agent orchestration
• Human-in-the-loop checkpoints
• Strategies for managing conflicts

Module 4 — Architecture, MCP & Advanced DevOps

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

Module 5 — Scaling Claude Code & Codebase Health

• Managing tokens and context
• AI-friendly project structures
• Long-term codebase maintainability
• Automating documentation
• Strategies for AI scalability
• Team-wide engineering workflows

Module 6 — Capstone: Defining Your Claude Code Process

• Designing scalable AI-assisted workflows
• Integrating prompts, commands, and context files
• Designing team AI processes
• Cross-role collaboration models
• Creating a workflow blueprint

Module 7 — Advanced Team Prompt Lab

• Developing advanced prompt libraries
• Complex role-specific workflows
• Validating prompts in real-world scenarios
• Cross-team collaboration exercises
• Team Prompt Library v2

Requirements

Day 1 — Foundation

• A fundamental grasp of software delivery processes
• A general understanding of development, testing, or agile workflows
• Access to Claude is advised for hands-on exercises

Day 2 — Advanced

• Completion of Day 1 or equivalent experience
• Previous exposure to Claude Code and prompt engineering concepts
• Basic knowledge of Git
• Familiarity with CI/CD concepts is recommended

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