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Duration 21 hours
Course Outline
Introduction to Claude Code & AI-Assisted Software Engineering
- Defining Claude Code and distinguishing it from traditional AI tools
- The role of generative AI agents within software engineering
- Constructing entire applications using large, complex prompts
- Assessing productivity improvements resulting from AI-assisted development
AI Labor & Software Engineering Productivity
- Viewing Claude Code as an AI development team member
- Addressing common concerns and misconceptions regarding AI in engineering
- Understanding the economics of AI labor
- Utilizing the Best-of-N pattern to generate multiple potential solutions
- Selecting and refining the most optimal implementations
Claude Code, Design, and Code Quality
- Evaluating AI's ability to judge code quality
- Applying software design principles with AI support
- Using AI to explore requirements and solution spaces
- Rapid prototyping through conversational design workflows
- Enhancing output quality by applying constraints and structured prompts
Process, Context, and the Model Context Protocol (MCP)
- Prioritizing process and context over raw code generation
- Establishing global persistent context using CLAUDE.md
- Structuring project rules, architecture, and constraints within context files
- Leveraging reusable, targeted context through Claude Code commands
- In-context learning by providing examples to teach Claude Code
Automation & Documentation with Claude Code
- Generating and maintaining documentation using Claude Code
- Automating repetitive engineering tasks
- Building reusable workflows driven by context and commands
Version Control & Parallel Development with Claude Code
- Integrating Claude Code into Git-based workflows
- Utilizing Git branches and worktrees alongside AI agents
- Executing Claude Code tasks in parallel
- Coordinating multiple AI subagents on distinct features
- Safely managing parallel feature development
Scaling Claude Code & AI Reasoning
- Acting as the hands, eyes, and ears for Claude Code
- Ensuring Claude Code performs self-review and verification
- Managing token limits and architectural complexity
- Designing project structures and file naming conventions for AI scalability
- Maintaining long-term codebase health with AI assistance
Multimodal Prompting & Process-Driven Development
- Resolving process and context issues before addressing code
- Translating informal inputs (notes, sketches, specs) into production-ready 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
- Synthesizing context files, commands, subagents, and prompts
- Creating a reusable, scalable AI-assisted engineering process
Requirements
- A solid understanding of software development principles and standard engineering workflows.
- Proficiency in a programming language such as JavaScript, Python, or similar.
- Experience with command line and terminal usage, along with familiarity with Git workflows.
Target Audience
- Software developers looking to integrate AI into their development processes.
- Technical team leads aiming to boost engineering productivity using AI tools.
- DevOps engineers and engineering managers interested in AI-assisted coding automation.
Testimonials (2)
The power of claude is the next gold in the IT space.
QINISO DLAMINI - Eswatini Revenue Service
Course - Claude for Coding
Chris did a phenomenal job of framing food for thought and facilitating team conversation on the various subjects.
