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Course Outline
Introduction to Claude Code & AI-Assisted Software Engineering
- Understanding what Claude Code is and how it differs from traditional AI tools
- The role of generative AI agents in software engineering
- Utilizing large prompts to build entire applications
- Recognizing productivity gains from AI-assisted development
AI Labor & Software Engineering Productivity
- Approaching Claude Code as an AI development team
- Addressing common fears and misconceptions about AI in engineering
- Understanding AI labor economics
- Leveraging the Best-of-N pattern to generate multiple solutions
- Selecting and refining optimal implementations
Claude Code, Design, and Code Quality
- Evaluating whether AI can effectively judge code quality
- Applying software design principles with AI assistance
- Using AI to explore requirements and solution spaces
- Engaging in rapid prototyping through conversational design workflows
- Applying constraints and structured prompts to improve output quality
Process, Context, and the Model Context Protocol (MCP)
- The importance of process and context over raw code generation
- Utilizing global persistent context via CLAUDE.md
- Structuring project rules, architecture, and constraints within context files
- Enabling reusable targeted context through Claude Code commands
- Facilitating in-context learning by teaching Claude Code with examples
Automation & Documentation with Claude Code
- Using Claude Code to generate and maintain documentation
- Automating repetitive engineering tasks
- Creating reusable workflows driven by context and commands
Version Control & Parallel Development with Claude Code
- Integrating Claude Code with Git-based workflows
- Utilizing Git branches and worktrees with AI agents
- Running Claude Code tasks in parallel
- Coordinating multiple AI subagents on separate features
- Managing parallel feature development securely
Scaling Claude Code & AI Reasoning
- Acting as Claude Code’s hands, eyes, and ears
- Ensuring Claude Code reviews and verifies its own work
- Managing token limits and architectural complexity
- Designing project structure and file naming conventions for AI scalability
- Maintaining long-term codebase health with AI assistance
Multimodal Prompting & Process-Driven Development
- Prioritizing process and context before addressing code issues
- Translating informal inputs (notes, sketches, specs) into production code
- Using multimodal inputs to guide implementation
- Creating repeatable AI-assisted development processes
Capstone: Defining Your Claude Code Process
- Designing a personal or team-level Claude Code workflow
- Combining context files, commands, subagents, and prompts
- Establishing a reusable, scalable AI-assisted engineering process
Requirements
- A solid understanding of software development principles and standard engineering workflows.
- Experience with programming languages such as JavaScript, Python, etc.
- Proficiency in command line / terminal usage and familiarity with Git workflows.
Target Audience
- Software developers looking to integrate AI into their development process.
- Technical team leads aiming to boost engineering productivity using AI tools.
- DevOps engineers and engineering managers interested in AI-assisted coding automation.
21 Hours
Testimonials (1)
Chris did a phenomenal job of framing food for thought and facilitating team conversation on the various subjects.
