Course Outline
Day 1 | Understanding the Tools and Initial Project Creation
Module 1 | How AI Coding Tools Function
Covered topics:
• Comprehending context windows and their constraints
• The concept of statelessness and how AI models retain information within a session
• The Plan → Execute → Review workflow
• Areas where AI coding tools excel and areas where they face challenges
• Best practices for effective collaboration with AI assistants
Module 2 | Overview of the AI Coding Ecosystem
Covered topics:
• General landscape of current AI coding ecosystems
• Key distinctions between tools such as Cursor, GitHub Copilot, and Claude Code
• Choosing the appropriate model and tool for specific tasks
• Strengths and limitations of various coding assistants
• Practical guidelines for adopting these tools within development teams
Module 3 | Structure of Effective Prompts
Covered topics:
• Essential components of a well-crafted prompt
• How to provide clear context and define the task explicitly
• Defining output formats and constraints
• Common prompting frameworks and templates
• Strategies for enhancing prompt quality and consistency
Module 4 | Initial Coding: Building from Scratch
Covered topics:
‡ Creating a project starting from an empty directory
‡ Establishing the initial application structure and scaffolding
‡ Managing dependencies and project configuration
‡ Iteratively refining generated code
‡ Testing and optimizing the final solution
Day 2 | Working with Existing Codebases, Personalization, and Review
Module 5 | Navigating an Existing Codebase
Covered topics:
‡ Understanding and navigating unfamiliar code structures
‡ Using AI tools to query and analyze existing projects
‡ Mapping application structure and dependencies
‡ Generating documentation and technical summaries
‡ Accelerating the onboarding process for new team members
Module 6 | Everyday Tasks: Bug Fixes, Features, and Testing
Covered topics:
‡ Using AI tools to investigate and resolve bugs
‡ Implementing new features and enhancements
‡ Writing and refining automated tests
‡ Validating generated code and proposed changes
‡ Boosting productivity in daily development activities
Module 7 | Understanding Personalization
Covered topics:
‡ Grasping project rules and configuration files
‡ Introduction to AGENTS.md and project memory concepts
‡ Identifying where and when personalization mechanisms apply
‡ Best practices for configuring AI assistants
‡ Overview of advanced implementation strategies
Module 8 | Guardrails, Risks, and Judgment
Covered topics:
‡ Reviewing and validating AI-generated code
‡ Recognizing common failure modes and limitations
‡ Identifying 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 experience with coding or AI tools is necessary.
Familiarity with basic coding concepts or Git is advantageous.
A licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
This course is ideal for newcomers to AI-assisted development, including non-programmers, occasional coders, and professionals in technical-adjacent fields 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
