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 Duration 14 hours

Course Outline

Understanding the Tools and Initial Build

How AI Coding Tools Function

Key topics include:
• Context windows and their inherent limitations
• Statelessness and how AI models maintain information throughout a session
• The Plan → Execute → Review workflow
• Capabilities and challenges of AI coding tools
• Best practices for effective collaboration with AI assistants

The AI Coding Ecosystem

Key topics include:
• An overview of the current AI coding landscape
• Distinctions between tools like Cursor, GitHub Copilot, and Claude Code
• Selecting appropriate models and tools for specific tasks
• Strengths and constraints of various coding assistants
• Practical advice for adopting tools within development teams

Deconstructing Prompts

Key topics include:
• Essential elements of effective prompting
• Providing clear context and defining tasks
• Specifying output formats and constraints
• Common prompting frameworks and templates
• Methods for enhancing prompt quality and consistency

Initial Coding: Building from Scratch

Key topics include:
• Developing a project starting from an empty directory
• Establishing initial application structure and scaffolding
• Managing dependencies and project settings
• Iteratively refining generated code
• Testing and polishing the final solution

Existing Codebases, Customization, and Review

Navigating Existing Codebases

Key topics include:
• Exploring and comprehending unfamiliar codebases
• Querying and analyzing existing projects with AI tools
• Mapping application architecture and dependencies
• Generating documentation and technical summaries
• Streamlining onboarding for existing projects

Routine Tasks: Debugging, Features, and Testing

Key topics include:
• Leveraging AI tools to identify and resolve bugs
• Developing new features and enhancements
• Creating and improving automated tests
• Verifying generated code and modifications
• Boosting productivity in daily development tasks

Customization: An Overview

Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Application of personalization mechanisms in different contexts
• Best practices for configuring AI assistants
• An overview of advanced implementation strategies

Guardrails, Risks, and Judgment

Key topics include:
• Reviewing and validating AI-generated code
• Understanding 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.

A basic understanding of code or Git is advantageous

A licensed account (Claude Code / Cursor / Copilot)

Target Audience:

Individuals new to AI-assisted development, ranging from non-coders and occasional developers to technical-adjacent roles in QA, data, product, or operations. No prior development background is assumed.

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