Course Outline
Day 1 Curriculum
Module 1 — Overview of Claude Code and AI-Enhanced Engineering
• Comparison of Claude Code with conventional AI utilities
• Application of AI agents within software engineering disciplines
• Optimization of productivity and operational workflows
• Integration into the AI-assisted development lifecycle
• Assessment of risks, limitations, and human oversight mechanisms
• Live practical demonstrations
Module 2 — Fundamentals of Prompt Engineering
• Structural analysis of effective prompts
• Distinction between zero-shot and few-shot prompting methods
• Techniques for iterative prompt refinement
• Introduction to prompt chaining strategies
• Management of structured outputs and formatting standards
• Processes for prompt verification and quality assurance
Module 3 — Prompting Strategies for Software Development
• Facilitating code generation and refactoring tasks
• Utilizing AI assistance for debugging processes
• Automated generation of technical documentation
• Assistance in pull request review procedures
• Interpretation of legacy codebases
• Ensuring the safety and maintainability of AI-generated code
Module 4 — Prompting for Testing and Quality Assurance
• Development of comprehensive test cases
• Identification and analysis of edge cases
• Designing automation-ready test frameworks
• AI-supported defect analysis and resolution
• Creation of Gherkin syntax and test scenarios
• Implementation of quality verification workflows
Module 5 — Prompting for Agile Collaboration
• Drafting user stories and acceptance criteria
• Refinement of project requirements
• Support for agile communication protocols
• Creation of executive and stakeholder summaries
• Facilitation of retrospective sessions
• Preparation for backlog refinement activities
Module 6 — Responsible AI, Security, and Verification
• Management of hallucinations and associated AI risks
• Protocols for confidentiality and secure prompting
• Principles of AI governance for government
• Implementation of verification checklists
• Awareness of prompt injection vulnerabilities
• Definition of human review responsibilities
Module 7 — Team Prompt Laboratory
• Construction of reusable organizational prompts
• Development of role-specific AI workflows
• Processes for prompt sharing and peer review
• Establishment of Team Prompt Library v1
• Execution of interactive collaborative exercises
Day 2
Module 1 — Advanced Capabilities of Claude Code
• Configuration of CLAUDE.md for persistent project context
• Automation of AI-driven workflows
• Implementation of Best-of-N generation strategies
• Creation of reusable AI command sets
• Application of context engineering techniques
• Integration of AI-assisted engineering workflows
Module 2 — Advanced Prompt Engineering Techniques
• Utilization of chain-of-thought prompting logic
• Application of multimodal prompting methods
• Employment of constraint-based prompting strategies
• Implementation of advanced prompt chaining
• Management of large-context data
• Development of conversational engineering workflows
Module 3 — Version Control, Parallel Development, and Multi-Agent Workflows
• Strategies for Git integration
• Management of parallel AI development processes
• Utilization of worktrees and isolated AI tasks
• Orchestration of multi-agent systems
• Establishment of human-in-the-loop checkpoints
• Strategies for managing code conflicts
Module 4 — Architecture, MCP, and Advanced DevOps
• Exploration of the Model Context Protocol (MCP)
• Integration of Claude with external operational tools
• AI-assisted analysis of system architecture
• Documentation of Architecture Decision Records (ADR)
• Troubleshooting CI/CD pipelines with AI support
• Conducting incident postmortems and operational reviews
Module 5 — Scaling Claude Code and Codebase Health
• Optimization of token and context resource management
• Establishment of AI-friendly project structures
• Ensuring long-term codebase maintainability
• Automation of technical documentation
• Development of AI scalability strategies
• Implementation of team-wide engineering workflows
Module 6 — Capstone: Defining the Claude Code Process
• Design of scalable AI-assisted operational workflows
• Synthesis of prompts, commands, and context files
• Design of team-specific AI processes for government
• Establishment of cross-role collaboration models
• Creation of comprehensive workflow blueprints
Module 7 — Advanced Team Prompt Laboratory
• Development of an advanced prompt library
• Implementation of complex role-specific workflows
• Validation of prompts in real-world operational scenarios
• Execution of cross-team collaboration exercises
• Finalization of Team Prompt Library v2
Requirements
Day 1 — Foundation
• Basic familiarity with software delivery processes
• General understanding of development, testing, or agile workflows
• Access to Claude is recommended for hands-on exercises
Day 2 — Advanced
• Completion of Day 1 (or equivalent experience)
• Prior exposure to Claude Code and prompt engineering concepts
• Basic proficiency in Git
• Familiarity with CI/CD concepts is recommended
Testimonials (2)
Learning how to prompt Claude and use it to digest all of the data I have available.
Mike Hartleroad - Furniture Row
Course - Claude AI for Data Analysis and Business Intelligence
how to engage with the Office environment and set up repetitive tasks
