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Course Outline

Overview of Claude Code and Artificial Intelligence in Software Engineering

  • Definition of Claude Code and distinctions from conventional AI utilities
  • Function of generative AI agents within software engineering workflows
  • Leveraging comprehensive prompts to develop full-scale applications
  • Evaluating productivity improvements in AI-assisted development for government

AI Workforce Integration and Engineering Efficiency

  • Operationalizing Claude Code as an automated engineering unit
  • Addressing concerns and clarifying misconceptions regarding AI in technical roles
  • Analysis of labor economics related to AI implementation
  • Application of the Best-of-N pattern to produce varied solution sets
  • Identification and refinement of optimal technical implementations

Technical Design and Code Quality Standards with Claude Code

  • Assessment of AI capabilities in evaluating code quality
  • Integration of software design principles through AI assistance
  • Utilization of AI to analyze requirements and explore solution spaces
  • Faster prototyping via interactive design workflows
  • Enhancing output quality through defined constraints and structured prompts

Workflow Management, Contextual Data, and the Model Context Protocol (MCP)

  • Priority of established processes and contextual data over raw code generation
  • Implementation of persistent global context using CLAUDE.md files
  • Organization of project rules, architectural standards, and constraints within context files
  • Deployment of reusable, targeted context through specific Claude Code commands
  • Facilitation of in-context learning via instructional examples for Claude Code

Automation and Documentation Capabilities of Claude Code

  • Generation and maintenance of technical documentation using Claude Code
  • Automation of routine engineering procedures
  • Establishment of reusable workflows driven by contextual data and command inputs for government operations

Version Control and Concurrent Development with Claude Code

  • Integration of Claude Code within Git-based version control systems
  • Utilization of Git branches and worktrees in conjunction with AI agents
  • Execution of multiple Claude Code tasks simultaneously
  • Coordination of distinct AI subagents across separate feature sets
  • Safeguarding parallel feature development activities

Scalability and Reasoning Processes in Claude Code

  • Operator roles acting as the interface for Claude Code’s sensory functions
  • Protocols for ensuring AI self-review and validation
  • Management of token limits and architectural complexity
  • Optimization of project structure and file naming conventions for AI scalability
  • Maintenance of long-term codebase integrity with AI support

Multimodal Inputs and Process-Oriented Development

  • Prioritization of process refinement and contextual accuracy before code modification
  • Conversion of informal inputs, such as notes and specifications, into production-ready code
  • Utilization of multimodal data to direct technical implementation
  • Development of repeatable AI-assisted development procedures for government standards

Capstone: Establishment of Custom Claude Code Workflows

  • Design of individual or organizational-level Claude Code workflows
  • Integration of context files, commands, subagents, and prompt strategies
  • Creation of scalable, reusable AI-assisted engineering processes

Requirements

  • Demonstrated proficiency in software engineering fundamentals and standard development lifecycles.
  • Practical experience utilizing programming languages including, but not limited to, JavaScript and Python.
  • Competency in command-line interfaces and established Git version control practices.

Audience

  • Software engineers focused on incorporating artificial intelligence into their technical workflows for government applications.
  • Technical leadership personnel dedicated to enhancing operational efficiency through AI-driven engineering solutions.
  • DevOps professionals and engineering managers exploring automated coding capabilities enabled by AI technologies.
 21 Hours

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