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 Duration 14 hours

Course Outline

Day 1 Curriculum

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

• Claude Code compared to traditional AI tools
• The role of AI agents in software engineering
• Optimizing productivity and workflows
• AI-driven development lifecycle
• Risks, constraints, and human supervision
• Live practical demonstrations

Module 2 — Prompt Engineering Essentials

• Structure of an effective prompt
• Zero-shot versus few-shot prompting
• Iterative prompting methods
• Fundamentals of prompt chaining
• Structured outputs and formatting
• Prompt validation and quality enhancement

Module 3 — Prompting for Software Development

• Code generation and refactoring
• AI-assisted debugging
• Generating documentation
• Reviewing pull requests
• Understanding legacy code
• Ensuring safe and maintainable AI-generated code

Module 4 — Prompting for Testing & Quality

• Generating test cases
• Analyzing edge cases
• Designing automation-ready tests
• AI-supported defect analysis
• Creating Gherkin and test scenarios
• Quality verification workflows

Module 5 — Prompting for Agile Collaboration

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

Module 6 — Responsible AI, Security & Verification

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

Module 7 — Team Prompt Lab

• Creating reusable team prompts
• Role-specific AI workflows
• Prompt sharing and peer review
• Establishing 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-driven engineering workflows

Module 2 — Advanced Prompt Engineering Methods

• Chain-of-thought prompting
• Multimodal prompting
• Constraint-based prompting
• Sophisticated 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 resolution strategies

Module 4 — Architecture, MCP & Advanced DevOps

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

Module 5 — Scaling Claude Code & Codebase Health

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

Module 6 — Capstone: Defining Your Claude Code Process

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

Module 7 — Advanced Team Prompt Lab

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

Requirements

Day 1 — Core Foundations

• Foundational knowledge of software delivery processes
• General comprehension of development, testing, or agile workflows
• Access to Claude is advised for practical exercises

Day 2 — Advanced Mastery

• Completion of Day 1 (or comparable professional experience)
• Previous exposure to Claude Code and prompt engineering principles
• Fundamental Git proficiency
• Familiarity with CI/CD concepts is recommended

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