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 Duration 14 hours

Course Outline

Understanding the Tools and a First Build

The Mechanics of AI Coding Tools

Key topics include:
• Grasping context windows and their inherent constraints
• Statelessness and how AI models retain information during a session
• The Plan → Execute → Review workflow
• Identifying strengths of AI coding tools and areas where they face challenges
• Best practices for effective collaboration with AI assistants

The AI Coding Landscape

Key topics include:
• An overview of the current AI coding ecosystem
• Distinguishing between tools like Cursor, GitHub Copilot, and Claude Code
• Selecting appropriate models and tools for specific tasks
• Evaluating the strengths and limitations of various coding assistants
• Practical recommendations for adopting tools within development teams

Deconstructing Prompts

Key topics include:
• Essential elements of a high-quality prompt
• Providing clear context and defining tasks precisely
• Specifying output formats and setting constraints
• Exploring common prompting frameworks and templates
• Techniques for enhancing prompt quality and consistency

Initial Coding: Building From Scratch

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

Existing Codebases, Personalisation and Review

Navigating a Codebase

Key topics include:
• Exploring and understanding unfamiliar codebases
• Utilizing AI tools to query and analyze existing projects
• Mapping application structures and dependencies
• Generating documentation and technical summaries
• Streamlining onboarding processes for existing projects

Everyday Tasks: Fix, Feature and Test

Key topics include:
• Leveraging AI tools to investigate and resolve bugs
• Implementing new features and enhancements
• Writing and improving automated tests
• Validating generated code and changes
• Boosting productivity in routine development tasks

Personalisation: Definition and Application

Key topics include:
• Understanding project rules and configuration files
• Introduction to AGENTS.md and project memory concepts
• Identifying when and where personalisation mechanisms are applicable
• Best practices for configuring AI assistants
• Overview of advanced implementation strategies

Guardrails, Risks and Judgement

Key topics include:
• Reviewing and validating AI-generated code
• Understanding common failure modes and limitations
• Recognizing prompt injection and security risks
• Determining which tasks can be safely delegated to AI
• Applying human judgement 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) is required

Target Audience:

This course is designed for newcomers to AI-assisted development, including non-coders, occasional developers, and professionals in technical-adjacent roles such as QA, data analysis, product management, or operations. No prior development background is expected.

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