Course Outline
Day 1 | Understanding the Tools and Initial Project Creation
Module 1 | How AI Coding Tools Function
Topics covered:
• Grasp of context windows and their inherent constraints
• The concept of statelessness and how AI models maintain information during a session
• The Plan → Execute → Review operational workflow
• Strengths and weaknesses 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 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 strategies for adopting tools within development teams
Module 3 | Components of Effective Prompts
Topics covered:
• Essential elements of a well-crafted prompt
• Providing clear context and defining the task explicitly
• Specifying output formats and necessary constraints
• Common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency
Module 4 | Initial Coding: Project Creation from Scratch
Topics covered:
• Setting up a project in an empty directory
• Establishing the initial application structure and scaffolding
• Managing dependencies and project configuration
• Iteratively refining the generated code
• Testing and optimizing the final solution
Day 2 | Handling Existing Codebases, Customization, and Review
Module 5 | Working Within a Codebase
Topics covered:
• Navigating and comprehending unfamiliar code structures
• Using AI tools to query and analyze existing projects
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Accelerating the onboarding process for new team members
Module 6 | Routine Tasks: Bug Fixes, Features, and Testing
Topics covered:
• Utilizing 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 daily development activities
Module 7 | Customization: Core Concepts
Topics covered:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Applicability of customization mechanisms
• Best practices for configuring AI assistants
• Overview of advanced implementation strategies
Module 8 | Guardrails, Risks, and Judgment
Topics covered:
• Reviewing and validating code generated by AI
• Understanding common failure modes and limitations
• Recognizing prompt injection and security risks
• Determining which tasks are suitable for AI delegation
• Applying human judgment and maintaining accountability in software development
Requirements
No prior experience with coding or AI tools is required.
Familiarity with basic code concepts or Git is beneficial.
A valid license account for Claude Code, Cursor, or Copilot is needed.
Audience:
This course is ideal for beginners in AI-assisted development, including non-programmers, occasional coders, and 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
