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 Duration 14 hours

Course Outline

Grasping the Tools and Initial Development

The Mechanics Behind AI Coding Tools

Key concepts addressed:
• Context windows and their inherent limitations
• Statelessness and the retention of information by AI models during a session
• The Plan → Execute → Review workflow
• Capabilities and challenges of AI coding tools
• Strategies for effective collaboration with AI assistants

The AI Coding Ecosystem

Key concepts addressed:
• An overview of the current AI coding landscape
• Distinguishing between tools like Cursor, GitHub Copilot, and Claude Code
• Choosing appropriate models and tools for specific tasks
• The strengths and limitations of various coding assistants
• Practical advice for integrating these tools into development teams

Anatomy of Effective Prompts

Key concepts addressed:
• Essential elements of a high-quality prompt
• Clarifying context and defining the task
• Defining output formats and constraints
• Common prompting frameworks and templates
• Methods for enhancing prompt quality and consistency

Initial Coding: Creating a Project from Scratch

Key concepts addressed:
• Developing a project starting from an empty directory
• Establishing the initial application structure and scaffolding
• Handling dependencies and project settings
• Refining generated code through iteration
• Testing and polishing the final output

Working with Existing Code, Customization, and Review

Navigating a Codebase

Key concepts addressed:
• Exploring and understanding unfamiliar codebases
• Using AI tools to query and analyze existing projects
• Mapping out application structure and dependencies
• Generating documentation and technical summaries
• Speeding up the onboarding process for existing projects

Routine Tasks: Debugging, Features, and Testing

Key concepts addressed:
• Leveraging AI tools to diagnose and resolve bugs
• Developing new features and improvements
• Creating and enhancing automated tests
• Verifying generated code and modifications
• Boosting efficiency in daily development activities

Customization: The Basics

Key concepts addressed:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Application of customization mechanisms in relevant contexts
• Best practices for setting up AI assistants
• An overview of advanced implementation strategies

Guardrails, Risks, and Critical Thinking

Key concepts addressed:
• Reviewing and validating AI-generated code
• Understanding common failure patterns and limitations
• Identifying prompt injection and security threats
• Determining which tasks can be delegated to AI
• Exercising human judgment and maintaining accountability in software development

Requirements

No previous coding or AI tool experience is necessary.

Basic knowledge of code or Git is beneficial

A licensed account (Claude Code / Cursor / Copilot)

Target Audience:

Individuals new to AI-assisted development, including non-coders, occasional coders, and professionals in technical-related roles such as QA, data, product, or operations. No prior development experience is expected.

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