Course Outline
Day 1 Outline
Module 1 — Introduction to Claude Code & AI-Assisted Engineering
• Comparison of Claude Code with traditional AI tools
• Role of AI agents in software engineering
• Productivity and workflow optimization
• AI-assisted development lifecycle overview
• Understanding risks, limitations, and the need for human oversight
• Live practical demonstrations
Module 2 — Prompt Engineering Fundamentals
• Components of an effective prompt
• Zero-shot versus few-shot prompting strategies
• Techniques for iterative prompting
• Basics of prompt chaining
• Managing structured outputs and formatting
• Verifying prompts and enhancing quality
Module 3 — Prompting for Software Development
• Code generation and refactoring techniques
• Debugging with AI assistance
• Automated documentation generation
• Utilizing AI for pull request reviews
• Understanding legacy codebases
• Ensuring safe and maintainable AI-generated code
Module 4 — Prompting for Testing & Quality
• Generating test cases
• Analyzing edge cases
• Designing automation-ready tests
• Analyzing defects with AI support
• Creating Gherkin syntax and test scenarios
• Implementing quality verification workflows
Module 5 — Prompting for Agile Collaboration
• Drafting user stories and acceptance criteria
• Refining requirements
• Supporting agile communication
• Preparing stakeholder summaries
• Assisting with retrospectives
• Preparing for backlog refinement
Module 6 — Responsible AI, Security & Verification
• Addressing hallucinations and AI-related risks
• Maintaining confidentiality through secure prompting
• Applying AI governance principles
• Using verification checklists
• Recognizing prompt injection threats
• Defining human review responsibilities
Module 7 — Team Prompt Lab
• Developing reusable team prompts
• Creating role-specific AI workflows
• Sharing prompts and conducting peer reviews
• Establishing a Team Prompt Library v1
• Participating in interactive collaborative exercises
Day 2
Module 1 — Claude Code Advanced Capabilities
• Utilizing CLAUDE.md for persistent project context
• Automating AI workflows
• Implementing best-of-N generation strategies
• Creating reusable AI commands
• Applying context engineering techniques
• Integrating AI-assisted engineering workflows
Module 2 — Advanced Prompt Engineering Techniques
• Employing chain-of-thought prompting
• Using multimodal prompting methods
• Applying constraint-based prompting
• Executing advanced prompt chaining
• Managing large-context interactions
• Designing conversational engineering workflows
Module 3 — Version Control, Parallel Development & Multi-Agent Workflows
• Implementing Git integration strategies
• Running parallel AI development workflows
• Using worktrees for isolated AI tasks
• Orchestrating multi-agent systems
• Establishing human-in-the-loop checkpoints
• Managing conflict resolution strategies
Module 4 — Architecture, MCP & Advanced DevOps
• Understanding the Model Context Protocol (MCP)
• Integrating Claude with external tools
• Conducting AI-assisted architecture analysis
• Documenting Architecture Decision Records (ADR)
• Troubleshooting CI/CD pipelines with AI support
• Facilitating incident postmortems and operational workflows
Module 5 — Scaling Claude Code & Codebase Health
• Managing tokens and context limits
• Designing AI-friendly project structures
• Ensuring long-term codebase maintainability
• Automating documentation processes
• Developing AI scalability strategies
• Aligning team-wide engineering workflows
Module 6 — Capstone: Define Your Claude Code Process
• Designing scalable AI-assisted workflows
• Combining prompts, commands, and context files
• Crafting team AI processes
• Establishing cross-role collaboration models
• Creating workflow blueprints
Module 7 — Advanced Team Prompt Lab
• Developing advanced prompt libraries
• Handling complex role-specific workflows
• Validating prompts in real-world scenarios
• Engaging in cross-team collaboration exercises
• Finalizing Team Prompt Library v2
Requirements
Day 1 — Foundations
• Basic understanding of software delivery processes
• General knowledge of development, testing, or agile workflows
• Access to Claude is recommended for hands-on exercises
Day 2 — Advanced
• Completion of Day 1 (or equivalent professional experience)
• Prior exposure to Claude Code and prompt engineering principles
• Fundamental Git knowledge
• Familiarity with CI/CD concepts is advisable