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 Duration 14 hours

Course Outline

Day 1 Curriculum

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

• Comparing Claude Code with traditional AI tools
• The role of AI agents in software engineering
• Optimizing productivity and workflows
• AI-supported development lifecycle
• Managing risks, limitations, and human oversight
• Live practical demonstrations

Module 2 — Foundations of Prompt Engineering

• Structure of effective prompts
• Zero-shot versus few-shot prompting
• Iterative prompting strategies
• Basics of prompt chaining
• Structured output and formatting
• Prompt validation and quality enhancement

Module 3 — Prompting for Software Development

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

Module 4 — Prompting for Testing & Quality Assurance

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

Module 5 — Prompting for Agile Collaboration

• Writing 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

• Mitigating hallucinations and AI risks
• Confidentiality and secure prompting practices
• AI governance principles
• Verification checklists
• Awareness of prompt injection threats
• Defining human review responsibilities

Module 7 — Team Prompt Laboratory

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

Day 2

Module 1 — Advanced Claude Code Capabilities

• Using CLAUDE.md for persistent project context
• Automating AI workflows
• Best-of-N generation techniques
• Creating reusable AI commands
• Context engineering methods
• Advanced AI-assisted engineering processes

Module 2 — Advanced Prompt Engineering Techniques

• Chain-of-thought prompting
• Multimodal prompting
• Constraint-based prompting
• Complex prompt chaining
• Managing large contexts
• Conversational engineering workflows

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

• Git integration strategies
• Parallel AI development workflows
• Utilizing worktrees and isolated AI tasks
• Multi-agent orchestration
• Implementing 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 architectural analysis
• Architecture Decision Records (ADR)
• AI-assisted CI/CD troubleshooting
• Incident post-mortems and operational workflows

Module 5 — Scaling Claude Code & Codebase Health

• Token and context management
• Structuring projects for AI compatibility
• Ensuring long-term codebase maintainability
• Automating documentation
• AI scalability strategies
• Organization-wide engineering workflows

Module 6 — Capstone: Defining Your Claude Code Process

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

Module 7 — Advanced Team Prompt Laboratory

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

Requirements

Day 1 — Core Fundamentals

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

Day 2 — Advanced Applications

• Completion of Day 1 or equivalent prior experience
• Familiarity with Claude Code and prompt engineering concepts
• Foundational knowledge of Git
• Understanding of CI/CD principles is advised

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