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 Duration 14 hours

Course Outline

Day 1 Curriculum

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

• Claude Code compared to conventional AI tools
• AI agents in software engineering
• Productivity gains and workflow optimization
• AI-supported development lifecycle
• Risks, limitations, and the role of human oversight
• Live practical demonstrations

Module 2 — Fundamentals of Prompt Engineering

• Structure of an effective prompt
• Zero-shot versus few-shot prompting
• Iterative prompting methodologies
• Basics of prompt chaining
• Structured outputs and formatting
• Prompt validation 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 safety and maintainability in AI-generated code

Module 4 — Prompting for Testing & Quality

• Test case generation
• Edge-case analysis
• Designing automation-ready tests
• AI-assisted defect analysis
• Creating Gherkin and test scenarios
• Quality validation workflows

Module 5 — Prompting for Agile Collaboration

• User stories and acceptance criteria
• Refining requirements
• Support for agile communication
• Summarizing for stakeholders
• Assistance with retrospectives
• Preparing for backlog refinement

Module 6 — Responsible AI, Security & Validation

• Hallucinations and AI-related risks
• Confidentiality and secure prompting
• AI governance principles
• Validation checklists
• Awareness of prompt injection
• Responsibilities of human review

Module 7 — Team Prompt Laboratory

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

Day 2

Module 1 — Advanced Claude Code Features

• CLAUDE.md and persistent project context
• Automating AI workflows
• Best-of-N generation strategies
• Reusable AI commands
• Context engineering techniques
• AI-supported 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
• Conflict management strategies

Module 4 — Architecture, MCP & Advanced DevOps

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

Module 5 — Scaling Claude Code & Codebase Health

• Token and context management
• AI-friendly project structures
• Long-term codebase maintainability
• Documentation automation
• AI scalability strategies
• Organization-wide engineering workflows

Module 6 — Capstone: Designing Your Claude Code Process

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

Module 7 — Advanced Team Prompt Laboratory

• Developing advanced prompt libraries
• Complex role-specific workflows
• Real-world prompt validation
• Cross-team collaboration exercises
• Team Prompt Library v2

Requirements

Day 1 — Foundations

• Fundamental knowledge of software delivery processes
• General awareness of development, testing, or agile workflows
• Access to Claude is recommended for practical exercises

Day 2 — Advanced

• Completion of Day 1 (or equivalent experience)
• Prior familiarity with Claude Code and prompt engineering concepts
• Basic proficiency in Git
• Familiarity with CI/CD concepts is advisable

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