Course Outline
Day 1 | Grasping the Tools and Your First Build
Module 1 | The Mechanics of AI Coding Tools
Key topics:
• Context windows and their inherent constraints
• Statelessness and how AI models maintain information across a session
• The Plan → Execute → Review methodology
• Capabilities of AI coding tools and common areas of difficulty
• Best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Ecosystem
Key topics:
• Overview of the current AI coding landscape
• Distinctions between tools such as Cursor, GitHub Copilot, and Claude Code
• Selecting appropriate models and tools for specific tasks
• Strengths and weaknesses of various coding assistants
• Practical advice for integrating tools into development teams
Module 3 | Deconstructing Prompts
Key topics:
• Essential elements of a high-quality prompt
• Establishing context and clearly defining tasks
• Defining output formats and constraints
• Standard prompting frameworks and templates
• Strategies for enhancing prompt consistency and quality
Module 4 | Initial Build: Creating from Scratch
Key topics:
• Developing a project starting from an empty directory
• Establishing the initial application structure and scaffolding
• Handling dependencies and project configuration
• Refining generated code through iterative improvements
• Testing and polishing the final solution
Day 2 | Engaging with Existing Code, Personalization, and Review
Module 5 | Navigating Existing Codebases
Key topics:
• Exploring and comprehending unfamiliar codebases
• Utilizing AI tools to query and analyze existing projects
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Streamlining the onboarding process for existing projects
Module 6 | Routine Development: Bugs, Features, and Tests
Key topics:
• Leveraging AI tools to diagnose and resolve bugs
• Developing new features and enhancements
• Authoring and optimizing automated tests
• Verifying generated code and changes
• Boosting productivity in daily development activities
Module 7 | Personalization: Fundamentals
Key topics:
• Comprehending project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Applicability of personalization mechanisms
• Best practices for setting up AI assistants
• Overview of advanced implementation methods
Module 8 | Guardrails, Risks, and Critical Thinking
Key topics:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Identifying prompt injection and security vulnerabilities
• Determining which tasks are suitable for AI delegation
• Exercising human judgment and maintaining accountability in software development
Requirements
No previous coding experience or familiarity with AI tools is necessary.
A basic understanding of code or Git is advantageous
Access to a licensed account (Claude Code / Cursor / Copilot)
Target Audience:
This course is designed for individuals new to AI-assisted development, including non-coders, occasional developers, and technical roles in QA, data, product, or operations. No prior development background is expected.
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
