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 Duration 14 hours

Course Outline

Day 1 Overview

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

• Comparing Claude Code with traditional AI tools
• AI agents in software engineering
• Enhancing productivity and workflow efficiency
• The AI-assisted development lifecycle
• Risks, limitations, and the role of human oversight
• Live practical demonstrations

Module 2 — Fundamentals of Prompt Engineering

• The structure of an effective prompt
• Zero-shot vs. few-shot prompting
• Iterative prompting methods
• Basics of prompt chaining
• Structured outputs and formatting
• Verifying prompts and improving quality

Module 3 — Prompting for Software Development

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

Module 4 — Prompting for Testing & Quality

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

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

Module 7 — Team Prompt Lab

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

Day 2

Module 1 — Advanced Capabilities of Claude Code

• CLAUDE.md and maintaining project context
• Automating AI workflows
• Best-of-N generation strategies
• Reusable AI commands
• Techniques for context engineering
• 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
• Orchestrating multiple agents
• Implementing human-in-the-loop checkpoints
• Strategies for conflict management

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
• Creating AI-friendly project structures
• Ensuring long-term codebase maintainability
• Automating documentation
• Strategies for AI scalability
• Team-wide engineering workflows

Module 6 — Capstone: Designing Your Claude Code Process

• Designing scalable AI-assisted workflows
• Combining prompts, commands, and context files
• Designing team AI processes
• Models for cross-role collaboration
• Creating workflow blueprints

Module 7 — Advanced Team Prompt Lab

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

Requirements

Day 1 — Foundations

• Basic knowledge of software delivery processes
• General grasp of development, testing, or agile workflows
• Access to Claude is recommended for hands-on exercises

Day 2 — Advanced Topics

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

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