Course Outline
Day 1 | Comprehending the Tools and Initial Build
Module 1 | How AI Coding Tools Operate
Topics covered:
• Understanding context windows and their inherent constraints
• The concept of statelessness and how AI models retain information within a session
• The Plan → Execute → Review workflow
• Capabilities and limitations of AI coding tools
• Best practices for effective collaboration with AI assistants
Module 2 | The AI Coding Ecosystem
Topics covered:
• Overview of the current landscape of AI coding solutions
• Key differences between tools like 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 integrating tools into development teams
Module 3 | Anatomy of a Prompt
Topics covered:
• Essential components of an effective prompt
• Providing adequate context and clearly defining the task
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency
Module 4 | Initial Coding: Building From Scratch
Topics covered:
• Constructing a project starting with an empty directory
• Creating 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 | Navigating a Codebase
Topics covered:
• 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 | Daily Tasks: Debugging, 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 modifications
• Boosting productivity in routine development tasks
Module 7 | Personalization: Foundations
Topics covered:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Where and when personalization mechanisms are applied
• 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
• 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 previous experience with coding or AI tools is necessary.
Familiarity with basic coding concepts or Git is beneficial.
A licensed account for Claude Code, Cursor, or Copilot is required.
Target Audience:
This course is ideal for beginners in AI-assisted development, including non-programmers and occasional coders, as well as technical-adjacent professionals in QA, data, 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
