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Course Outline
Introduction to Claude Code & AI-Assisted Software Engineering
- What Claude Code is and how it differs from traditional AI tools
- The role of generative AI agents in software engineering
- Using large prompts to build entire applications
- Understanding productivity gains from AI-assisted development
AI Labor & Software Engineering Productivity
- Treating 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 judge code quality
- Applying software design principles with AI assistance
- Using AI to explore requirements and solution spaces
- Rapid prototyping with 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
- Global persistent context using CLAUDE.md
- Structuring project rules, architecture, and constraints in context files
- Reusable targeted context through Claude Code commands
- 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
- Running 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 checks 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 fixing code
- 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
- An understanding of software development principles and common engineering workflows.
- Experience with a programming language such as JavaScript, Python, etc.
- Command line / terminal usage experience and familiarity with Git workflows.
Audience
- Software developers seeking to integrate AI into their development process.
- Technical team leads aiming to improve engineering productivity with AI tools.
- DevOps engineers and engineering managers interested in AI-assisted coding automation.
21 Hours
Testimonials (1)
Lecturer's knowledge in advanced usage of copilot & Sufficient and efficient practical session
