Course Outline
Day 1 Outline
Module 1 — Introduction to Claude Code & AI-Assisted Engineering
• Comparing Claude Code with traditional AI tools
• The role of AI agents in software engineering
• Enhancing productivity and workflow optimization
• Overview of the AI-assisted development lifecycle
• Understanding risks, limitations, and the necessity of human oversight
• Live practical demonstrations
Module 2 — Fundamentals of Prompt Engineering
• Deconstructing 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
• Automating documentation generation
• Conducting pull request reviews
• Gaining insight into legacy code
• Ensuring safe and maintainable AI-generated code
Module 4 — Prompting for Testing & Quality Assurance
• Generating test cases
• Analyzing edge cases
• Designing tests ready for automation
• AI-assisted defect analysis
• Creating Gherkin syntax and test scenarios
• Establishing quality verification workflows
Module 5 — Prompting for Agile Collaboration
• Crafting 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 risks
• Ensuring confidentiality and secure prompting
• Principles of AI governance
• Utilizing verification checklists
• Awareness of prompt injection threats
• Defining human review responsibilities
Module 7 — Team Prompt Lab
• Constructing reusable team prompts
• Developing role-specific AI workflows
• Facilitating prompt sharing and peer reviews
• Creating the initial Team Prompt Library v1
• Engaging in interactive collaborative exercises
Day 2
Module 1 — Advanced Capabilities of Claude Code
• Using CLAUDE.md for persistent project context
• Automating AI workflows
• Implementing Best-of-N generation strategies
• Creating reusable AI commands
• Techniques for context engineering
• Integrating AI-assisted engineering workflows
Module 2 — Advanced Prompt Engineering Techniques
• Chain-of-thought prompting methods
• Multimodal prompting approaches
• Constraint-based prompting
• Advanced prompt chaining strategies
• Managing large contexts
• Implementing conversational engineering workflows
Module 3 — Version Control, Parallel Development & Multi-Agent Workflows
• Strategies for Git integration
• Managing parallel AI development workflows
• Utilizing worktrees for isolated AI tasks
• Orchestrating multi-agent systems
• Establishing human-in-the-loop checkpoints
• Strategies for conflict management
Module 4 — Architecture, MCP & Advanced DevOps
• Understanding the Model Context Protocol (MCP)
• Integrating Claude with external tools
• AI-assisted architecture analysis
• Documenting Architecture Decision Records (ADR)
• AI-assisted CI/CD troubleshooting
• Conducting incident postmortems and operational workflows
Module 5 — Scaling Claude Code & Maintaining Codebase Health
• Managing tokens and context limits
• Structuring projects to be AI-friendly
• Ensuring long-term codebase maintainability
• Automating documentation
• Implementing AI scalability strategies
• Standardizing team-wide engineering workflows
Module 6 — Capstone: Defining Your Claude Code Process
• Designing scalable AI-assisted workflows
• Combining prompts, commands, and context files
• Structuring team AI processes
• Developing 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
• Facilitating cross-team collaboration exercises
• Finalizing Team Prompt Library v2
Requirements
Day 1 — Foundation
• 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
