Course Outline
Day 1 | Understanding the Tools and Building Your First Project
Module 1 | How AI Coding Tools Actually Work
Covered topics:
• Understanding context windows and their limitations
• Statelessness and how AI models retain information during a session
• The Plan → Execute → Review workflow
• Capabilities of AI coding tools and areas where they struggle
• Best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Landscape
Covered topics:
• Overview of the current AI coding ecosystem
• Key differences between tools like Cursor, GitHub Copilot, and Claude Code
• Selecting the appropriate model and tool for specific tasks
• Strengths and limitations of various coding assistants
• Practical recommendations for adopting tools within development teams
Module 3 | The Anatomy of a Prompt
Covered topics:
• Essential components of an effective prompt
• Providing context and defining tasks clearly
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for improving prompt quality and consistency
Module 4 | First Coding: Building from Scratch
Covered topics:
• Creating a project in an empty folder
• Establishing the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining generated code
• Testing and polishing the final solution
Day 2 | Working with Existing Codebases, Personalization, and Review
Module 5 | Working Within a Codebase
Covered topics:
• Navigating and comprehending an unfamiliar codebase
• Querying and analyzing existing projects using AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating onboarding into ongoing projects
Module 6 | Everyday Tasks: Debugging, Features, and Testing
Covered topics:
• Using AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating generated code and changes
• Boosting productivity in day-to-day development tasks
Module 7 | Personalization: Concepts and Application
Covered topics:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Where and when personalization mechanisms apply
• Best practices for configuring AI assistants
• Overview of advanced implementation approaches
Module 8 | Guardrails, Risks, and Judgment
Covered topics:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognizing prompt injection and security risks
• Determining which tasks can be delegated to AI
• Applying human judgment and maintaining accountability in software development
Requirements
No prior coding experience or familiarity with AI tools is required.
Basic knowledge of code or Git is beneficial.
A licensed account for Claude Code, Cursor, or Copilot is needed.
Audience:
This course is designed for those new to AI-assisted development, including non-coders, occasional programmers, and professionals in technical-adjacent roles such as QA, data analysis, product management, or operations. No prior development background is assumed.
Testimonials (2)
Learning how to prompt Claude and use it to digest all of the data I have available.
Mike Hartleroad - Furniture Row
Course - Claude AI for Data Analysis and Business Intelligence
how to engage with the Office environment and set up repetitive tasks
