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 Duration 14 hours

Course Outline

Day 1 Structure

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

• Differentiating Claude Code from conventional AI utilities
• The role of AI agents in software engineering
• Enhancing productivity and optimizing workflows
• Integrating AI into the development lifecycle
• Assessing risks, limitations, and the need for human oversight
• Live practical demonstrations

Module 2 — Core Principles of Prompt Engineering

• Structure and components of an effective prompt
• Comparing zero-shot and few-shot prompting
• Iterative refinement techniques
• Basics of prompt chaining
• Controlling output structure and formatting
• Validating prompts and enhancing quality

Module 3 — Prompting for Software Development

• Generating and refactoring code
• AI-aided debugging processes
• Automating documentation creation
• Reviewing pull requests with AI
• Interpreting legacy codebases
• Ensuring AI-generated code is safe and maintainable

Module 4 — Prompting for Testing & Quality Assurance

• Generating test cases
• Analyzing edge cases
• Designing tests ready for automation
• Assisting with defect analysis
• Creating Gherkin scenarios and test cases
• Workflows for quality verification

Module 5 — Prompting for Agile Collaboration

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

Module 6 — Responsible AI, Security & Verification

• Addressing hallucinations and AI risks
• Maintaining confidentiality and secure prompting
• Principles of AI governance
• Implementing verification checklists
• Understanding prompt injection threats
• Defining human review responsibilities

Module 7 — Team Prompt Laboratory

• Developing reusable team prompts
• Creating role-specific AI workflows
• Sharing prompts and peer reviews
• Establishing the initial Team Prompt Library
• Engaging in interactive collaborative exercises

Day 2

Module 1 — Advanced Capabilities of Claude Code

• 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 — Sophisticated Prompt Engineering Techniques

• Utilizing chain-of-thought prompting
• Multimodal prompting strategies
• Constraint-based prompting
• Advanced prompt chaining methods
• Managing large context windows
• Designing conversational engineering workflows

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

• Strategies for Git integration
• Managing parallel AI development workflows
• Using worktrees for isolated AI tasks
• Orchestrating multi-agent systems
• Incorporating human-in-the-loop checkpoints
• Handling conflict management

Module 4 — Architecture, MCP & Advanced DevOps

• The Model Context Protocol (MCP)
• Integrating Claude with external tools
• Conducting AI-assisted architecture analysis
• Managing Architecture Decision Records (ADR)
• Troubleshooting CI/CD with AI assistance
• Processing incident postmortems and operational workflows

Module 5 — Scaling Claude Code & Codebase Integrity

• Managing tokens and context
• Designing AI-friendly project structures
• Ensuring long-term codebase maintainability
• Automating documentation
• Strategies for AI scalability
• Implementing team-wide engineering workflows

Module 6 — Capstone: Designing Your Claude Code Process

• Architecting scalable AI-assisted workflows
• Integrating prompts, commands, and context files
• Designing team AI processes
• Modeling cross-role collaboration
• Creating a workflow blueprint

Module 7 — Advanced Team Prompt Laboratory

• Developing an advanced prompt library
• Managing complex role-specific workflows
• Validating prompts in real-world scenarios
• Engaging in cross-team collaboration exercises
• Finalizing the Team Prompt Library v2

Requirements

Day 1 — Foundations

• Fundamental awareness of software delivery methodologies
• General comprehension of development, testing, or agile processes
• Access to Claude is advised for practical exercises

Day 2 — Advanced Level

• Completion of Day 1 (or comparable practical experience)
• Prior encounter with Claude Code and prompt engineering principles
• Basic proficiency with Git
• Knowledge of CI/CD concepts is recommended

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