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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.
- Building entire applications using comprehensive prompts.
- 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 the economics of AI labor.
- Leveraging the Best-of-N pattern to generate multiple solutions.
- Selecting and refining the most 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.
- 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.
- Maintaining global persistent context using CLAUDE.md.
- Structuring project rules, architecture, and constraints within context files.
- Utilizing reusable, targeted context through Claude Code commands.
- Enabling 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.
- Using Git branches and worktrees with AI agents.
- Executing Claude Code tasks in parallel.
- Coordinating multiple AI subagents on separate features.
- Managing parallel feature development safely.
Scaling Claude Code & AI Reasoning
- Acting as Claude Code’s hands, eyes, and ears.
- Ensuring Claude Code reviews and validates its own work.
- Managing token limits and architectural complexity.
- Designing project structure and file naming for AI scalability.
- Maintaining long-term codebase health with AI assistance.
Multimodal Prompting & Process-Driven Development
- Fixing 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.
- Creating a reusable, scalable AI-assisted engineering process.
Requirements
- A foundational understanding of software development principles and common engineering workflows.
- Experience with a programming language such as JavaScript, Python, or similar.
- Familiarity with command line/terminal usage and Git workflows.
Target Audience
- Software developers looking to incorporate 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.
