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Course Outline
Day 1 Agenda
Module 1 — Overview of Claude Code and AI-Enhanced Engineering
• Comparative analysis of Claude Code against conventional AI utilities
• Application of AI agents within software engineering frameworks
• Optimization of productivity and operational workflows
• Implementation of AI-supported development lifecycles for government operations
• Identification of risks, constraints, and requirements for human oversight
• Conduct of live technical demonstrations
Module 2 — Fundamentals of Prompt Engineering
• Structural components of effective prompts
• Differentiation between zero-shot and few-shot prompting methods
• Techniques for iterative prompt refinement
• Principles of prompt chaining
• Utilization of structured outputs and data formatting
• Procedures for prompt verification and quality assurance
Module 3 — Application of Prompts in Software Development
• Strategies for code generation and refactoring
• AI-supported debugging processes
• Automated documentation creation
• Assistance with pull request evaluations
• Analysis of legacy code systems
• Standards for secure and maintainable AI-generated code
Module 4 — Application of Prompts in Testing and Quality Assurance
• Generation of test cases
• Analysis of edge-case scenarios
• Design of tests suitable for automation
• AI-assisted defect analysis
• Creation of Gherkin syntax and test scenarios
• Implementation of quality verification workflows
Module 5 — Facilitation of Agile Collaboration via Prompting
• Development of user stories and acceptance criteria
• Refinement of project requirements
• Support for Agile communication channels
• Preparation of stakeholder summaries
• Assistance with retrospective sessions
• Preparation for backlog refinement activities
Module 6 — Responsible AI Practices, Security, and Verification
• Mitigation of hallucinations and associated AI risks
• Maintenance of confidentiality through secure prompting protocols
• Adherence to AI governance principles within government contexts
• Implementation of verification checklists
• Awareness and prevention of prompt injection threats
• Clarification of human review responsibilities
Module 7 — Collaborative Team Prompt Laboratory
• Development of reusable team-level prompts
• Design of role-specific AI workflows for government applications
• Protocols for prompt sharing and peer review
• Establishment of an initial Team Prompt Library
• Execution of interactive collaborative exercises
Day 2 Agenda
Module 1 — Advanced Capabilities of Claude Code
• Configuration of CLAUDE.md files for persistent project context
• Automation of AI-driven workflows
• Implementation of best-of-N generation strategies
• Creation of reusable AI commands
• Techniques for context engineering
• Optimization of AI-assisted engineering processes
Module 2 — Advanced Prompt Engineering Techniques
• Utilization of chain-of-thought prompting methodologies
• Application of multimodal prompting
• Implementation of constraint-based prompting
• Execution of advanced prompt chaining
• Management of large-context data sets
• Integration of conversational engineering workflows
Module 3 — Version Control, Parallel Development, and Multi-Agent Workflows
• Strategies for Git integration
• Execution of parallel AI development workflows
• Use of worktrees and isolation of AI tasks
• Orchestration of multi-agent systems
• Implementation of human-in-the-loop checkpoint procedures
• Management of development conflicts
Module 4 — Architecture, Model Context Protocol (MCP), and Advanced DevOps
• Introduction to the Model Context Protocol (MCP)
• Integration of Claude with external government tools
• AI-supported architecture analysis
• Maintenance of Architecture Decision Records (ADR)
• Troubleshooting CI/CD pipelines using AI assistance
• Management of incident postmortems and operational workflows
Module 5 — Scalability of Claude Code and Codebase Health
• Management of tokens and contextual limits
• Development of AI-compatible project structures
• Strategies for long-term codebase maintainability
• Automation of documentation processes
• AI scalability strategies for federal IT environments
• Establishment of team-wide engineering workflows
Module 6 — Capstone: Definition of Claude Code Processes
• Design of scalable AI-assisted workflows
• Integration of prompts, commands, and context files
• Design of team AI processes for government entities
• Development of cross-role collaboration models
• Creation of comprehensive workflow blueprints
Module 7 — Advanced Team Prompt Laboratory
• Development of advanced prompt libraries
• Execution of complex role-specific workflows
• Validation of prompts in real-world scenarios
• Facilitation of cross-team collaboration exercises
• Establishment of the second iteration of the Team Prompt Library
Requirements
Day 1: Foundational Concepts
• Introduction to standard software delivery lifecycles
• Overview of development, quality assurance, and agile methodologies
• Access to Claude is recommended to facilitate practical application exercises for government agencies
Day 2: Advanced Technical Competencies
• Prerequisite completion of Day 1 or demonstrated equivalent professional experience
• Prior engagement with Claude Code tools and prompt engineering principles
• Fundamental proficiency in Git version control
• Familiarity with Continuous Integration/Continuous Deployment (CI/CD) frameworks is advised to ensure operational readiness
14 Hours