Course Outline
Day 1 | Understanding the Tools and Building Your First Project
Module 1 | How AI Coding Tools Actually Work
Topics covered:
• Understanding context windows and their limitations
• Statelessness and how AI models retain information during a session
• The Plan → Execute → Review workflow
• Areas where AI coding tools excel and where they may struggle
• Best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Landscape
Topics covered:
• Overview of the current AI coding ecosystem
• Understanding the differences between tools such as 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 these tools within development teams
Module 3 | Prompt Anatomy
Topics covered:
• Key components of an effective prompt
• Providing context and clearly defining the task
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for improving prompt quality and consistency
Module 4 | First Coding: Building from Scratch
Topics covered:
• Building a project starting from an empty folder
• Creating the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively improving generated code
• Testing and refining the final solution
Day 2 | Working with Existing Codebases, Personalization, and Review
Module 5 | Working in a Codebase
Topics covered:
• Navigating and understanding unfamiliar codebases
• Querying and analyzing existing projects using AI tools
• Mapping application structure and dependencies
• Generating documentation and technical summaries
• Accelerating onboarding into existing projects
Module 6 | Everyday Tasks: Fixes, Features, and Testing
Topics covered:
• Using AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating generated code and changes
• Increasing productivity in day-to-day development tasks
Module 7 | Personalization: What It Is
Topics covered:
• 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
Topics covered:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognizing prompt injection and security risks
• Deciding what work can be delegated to AI
• Applying human judgment and maintaining accountability in software development
Requirements
There is no requirement for prior coding or AI-tool experience.
Basic familiarity with code or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot is required.
Audience:
This course is ideal for newcomers 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
